Affective differentiation in breast cancer patients.
ABSTRACT Fifty-three breast cancer patients completed an Internet-based diary measuring daily negative affect and positive affect and daily negative and positive events for seven consecutive evenings shortly after surgery. The authors used Hierarchical Linear Modeling (Raudenbush and Bryk in Hierarchical linear models: applications and data analysis methods. Sage, Thousand Oaks, CA, 2002) to examine moderators of affective differentiation, or the daily relationship between the patients' negative affect and positive affect. Strong affective differentiation is characterized by the relative independence of negative and positive affect. There were no significant Level 1 (within-subject) moderators of affective differentiation. However, at Level 2 (between-subject), as predicted, increased age was associated with stronger affective differentiation, as was greater use of planning to cope with breast cancer. Also as predicted, increased anxiety and greater use of behavioral disengagement and denial coping were associated with weaker affective differentiation. The results suggest the value of the affective differentiation construct, and a daily diary methodology, for research on the daily lives of breast cancer patients.
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ABSTRACT: Aim The international literature rarely concerns itself with the refusal of treatment. We have studied the adaptation and subconscious defence mechanisms triggered in patients at the time of proposing adjuvant chemotherapy after cancer surgery. Materials and methods This longitudinal clinical study based on 50 breast cancer patients compares the psychological factors involved in the making of decisions following oncological consultation. Results Eighty-two percent of patients accepted chemotherapy while 18% refused it. The Accepting group usually exhibits depression in its reaction whereas the Refusing group generally has more hostile traits. Anxiety, significantly higher in the Accepting group than in the Refusing group, seems to be linked to feelings that are repressed by the patient that submits to the medical advice. Conversely, patients refusing the treatment tend to exhibit their hostility towards doctors and medicine verbally. Conclusion Changes in doctor- patient communication, coupled with a greater knowledge of the personality of patients, may help encourage patients to accept the treatment offered to them.Oncologie 15(1). · 0.10 Impact Factor
Affective differentiation in breast cancer patients
Kimberly B. Dasch•Lawrence H. Cohen•
Amber Belcher•Jean-Philippe Laurenceau•
Jeff Kendall•Scott Siegel•Brendt Parrish•
Received: November 18, 2009/Accepted: June 11, 2010/Published online: June 29, 2010
? Springer Science+Business Media, LLC 2010
Internet-based diary measuring daily negative affect and
positive affect and daily negative and positive events for
seven consecutive evenings shortly after surgery. The au-
thors used Hierarchical Linear Modeling (Raudenbush and
Bryk in Hierarchical linear models: applications and data
analysis methods. Sage, Thousand Oaks, CA, 2002) to
examine moderators of affective differentiation, or the
daily relationship between the patients’ negative affect and
positive affect. Strong affective differentiation is charac-
terized by the relative independence of negative and
positive affect. There were no significant Level 1 (within-
subject) moderators of affective differentiation. However,
at Level 2 (between-subject), as predicted, increased age
was associated with stronger affective differentiation, as
was greater use of planning to cope with breast cancer.
Also as predicted, increased anxiety and greater use of
behavioral disengagement and denial coping were associ-
ated with weaker affective differentiation. The results
suggest the value of the affective differentiation construct,
and a daily diary methodology, for research on the daily
lives of breast cancer patients.
Fifty-three breast cancer patients completed an
Breast cancer ? Affective differentiation ?
Breast cancer is the most frequently diagnosed cancer in
women and is responsible for the second largest number of
cancer-related deaths in women (American Cancer Society
2009). Diagnosis and treatment for breast cancer are sig-
nificant life stressors for women affected by the disease,
often resulting in psychological distress (e.g., Hinnen et al.
2008). Many studies have evaluated predictors of the
psychological functioning of breast cancer patients,
including, for example, demographic variables, pre-cancer
psychiatric diagnosis, recent life events, and perceived
social support (Green et al. 2000).
Few studies, however, have examined the daily psy-
chological experiences of breast cancer patients, experi-
ences that are best studied with a daily assessment (diary)
methodology. Compared to a typical cross-sectional
methodology that relies on relatively long-term retrospec-
tive reports, a daily diary methodology can increase the
reliability and validity of self-reports, because in the latter
methodology, there is a shorter lag between the experience
and reporting of affect and life experiences (Bolger et al.
2003). At present, we are aware of only one published
study with breast cancer patients that used a daily diary
methodology (Badr et al. 2006). Badr et al. studied 23
breast cancer survivors several years post-treatment and
found that greater daily fatigue and pain were associated
with greater daily negative mood.
One way to study daily experiences is to evaluate the daily
relationship between positive affect and negative affect,
that is, the extent to which they operate independently or in
a dependent, inverse manner. The literature on the rela-
K. B. Dasch ? L. H. Cohen (&) ? A. Belcher ?
J.-P. Laurenceau ? B. Parrish ? E. Graber
Department of Psychology, University of Delaware, Newark,
DE 19716, USA
J. Kendall ? S. Siegel
Helen F. Graham Cancer Center, Newark, DE, USA
J Behav Med (2010) 33:441–453
tionship between positive affect and negative affect is
extremely complex. Specifically, some studies suggest that
they operate independently, whereas other studies suggest
that they are inversely related (e.g., Reich et al. 2003). The
Dynamic Model of Affect (Reich et al. 2003) represents a
unique perspective on the relationship between positive
and negative affect because it is concerned with the per-
sonal and environmental determinants of the two affects’
interrelationship. In other words, the model does not ask
whether positive and negative affect are related, but instead
asks under what conditions are they related, and for which
The Dynamic Model of Affect proposes that, in general,
the relative independence (separation) of positive and
negative affect is adaptive: When people keep separate
accounts of their positive and negative affect, they main-
tain maximum information about their affective experi-
ence, because the presence of one emotion is not affected
by the presence or absence of another emotion. Ordinarily,
people benefit from having independent experiences of
negative and positive affect, resulting in a low correlation
between the two.
However, the cognitive load is greater when affective
differentiation is greater because there is more information
to process (i.e., separate experiences of positive affect and
negative affect). During times of stress, the attentional
resources become focused on the more immediate
demands, which provide competition for the cognitive
resources needed for affective differentiation. Therefore,
during times of stress, the Dynamic Model of Affect pre-
dicts that positive affect and negative affect will collapse
into a single bipolar dimension, resulting in an inverse
correlation between the two, and consequently weaker
affective differentiation (Reich et al. 2003). Further, the
ability to achieve affective differentiation is likely linked to
emotion regulation skills because a number of aspects of
emotion regulation seem relevant, such as the ability to
identify, understand, process, and express emotions (Davis
et al. 2004).
The major goal of the current study was to apply the
Dynamic Model of Affect to research on the daily emo-
tional experiences of breast cancer patients. Assuming that
it is adaptive to have relative independent experiences of
positive and negative affect, an understanding of the factors
that promote this independence in breast cancer patients
might contribute to the design of relevant prevention and
intervention programs (Reich et al. 2003). Specifically,
most psychological interventions focus on alleviation of
negative affect, sometimes at the expense of attention to
positive affect (Reich et al. 2003). Even interventions that
have emanated from the recent popularity of positive
psychology, and that are designed to promote positive
affect, rarely address how negative and positive affect
operate together in an integrative model (Reich et al.
To date, most of the research on affective differentiation
has been conducted by Zautra et al. with chronic pain pa-
tients, specifically those with rheumatoid arthritis and
fibromyalgia, and has relied on repeated assessments over
several weeks. In general, this research has supported the
model described above and suggests the adaptive value of
Within-subject moderators of affective differentiation
Some studies have examined within-subject (situational)
moderators of affective differentiation. For example, dur-
ing times of high stress, as well as high pain, the rela-
tionship between negative affect and positive affect
becomes stronger compared to times of low stress and low
pain (e.g., Potter et al. 2000). The presence of positive
affect weakens the relationship between pain and negative
affect (e.g., Zautra et al. 2001). In addition, research on
adults with chronic pain has shown that affective differ-
entiation is weaker on days associated with a greater
number of negative events, and is stronger on days asso-
ciated with a greater number of positive events (Zautra
et al. 2005a).
Between-subject moderators of affective differentiation
Several between-subject moderators of affective differen-
tiation have also been studied. This research suggests that
stronger affective differentiation is associated with adap-
tive individual difference variables, specifically higher
mood clarity (Zautra et al. 2001), and higher levels of
dispositional resilience and lower levels of neuroticism and
perceived stress (Ong and Bergeman 2004).
Although affective differentiation has been studied in a
few medical populations, primarily with chronic pain, to
our knowledge, it has not been examined in a cancer
population. In addition to the variables previously cited
(e.g., neuroticism and perceived stress), there are other
potential moderators of affective differentiation that seem
particularly relevant to breast cancer patients. We discuss
these potential moderators in the sections below.
Age and breast cancer
Numerous studies have documented that, compared to
older breast cancer patients, younger patients have a more
difficult time adjusting to their illness and treatment (e.g.,
Stanton et al. 2002; Turner et al. 2005). One reason why
younger patients experience more distress is that some
concerns are specific to them, for example, feeling different
442J Behav Med (2010) 33:441–453
than other woman their age and worrying that they will not
see their children grow up (Dunn and Steginga 2000). With
this in mind, we anticipated that in the current study,
affective differentiation would be positively related to pa-
tients’ age. In other words, we expected that, the younger
the patient, the more strongly daily negative affect and
positive affect would be inversely related.
Coping and breast cancer
Use of specific coping strategies influences adjustment to
breast cancer, and thus is another potential moderator of
patients’ affective differentiation. In general, active coping
is defined as taking concrete, active steps to solve a problem
or reduce its effects (Carver 1997). Overall, most active
types of coping are associated with positive psychological
outcomes in breast cancer patients (e.g., Bellizzi and Blank
2006; Epping-Jordan et al. 1999; Karanci and Erkam 2007).
Positive reframing coping is also associated with psycho-
logical adjustment in breast cancer patients (e.g., Karad-
emas et al. 2007; Reddick et al. 2005; Roussi et al. 2007;
Sears et al. 2003). Finally, several studies have also shown
that acceptance coping is associated with positive psycho-
logical outcomes in breast cancer patients (e.g., Culver et al.
2002; Roussi et al. 2007; Stanton et al. 2000).
Several types of coping strategies are consistently
associated with negative psychological outcomes in breast
cancer patients. These include disengagement forms of
coping (Compas et al. 2006; Culver et al. 2002; Epping-
Jordan et al. 1999), avoidant coping (Karademas et al.
2007; McCaul et al. 1999; Stanton et al. 2000; Stanton and
Snider 1993), and denial (Roussi et al. 2007).
In the current study, we measured patients’ coping with
their cancer experience and then related their coping
behavior with their daily affective differentiation. With the
aforementioned literature in mind, we expected that active
coping (including planning), positive reframing, and
acceptance coping would be associated with stronger
affective differentiation, whereas behavioral disengage-
ment, avoidance, and denial coping would be associated
with weaker affective differentiation. Our rationale for
these hypotheses is as follows: Because the former types of
coping (e.g., planning) have been shown to be adaptive for
cancer patients, their use should help patients successfully
navigate the daily stress associated with their illness and
treatment, and thus facilitate the daily availability of
attentional resources needed for affective differentiation.
On the other hand, because the latter types of coping (e.g.,
denial) have been shown to be maladaptive for cancer
patients, their use should be less helpful in managing pa-
tients’ daily stress, thus hindering the availability of
attentional resources needed for affective differentiation
(Reich et al. 2003).
Patients were recruited shortly after their breast cancer
surgery. They completed an initial packet of question-
naires, including a brief measure of demographic infor-
mation, as well as a measure of anxiety and a measure of
their use of coping strategies to deal with the cancer
experience. Following completion of this initial packet of
questionnaires, patients then completed an Internet-based
diary every night for seven consecutive nights, which
included items for daily negative affect and positive
affect and the daily occurrence of negative and positive
We used Hierarchical Linear Modeling (Raudenbush
and Bryk 2002) to examine the moderators of affective
differentiation. On a daily within-subject level (Level 1),
we examined the moderating effects of daily number of
negative and positive events. On a between-subject level
(Level 2), we examined the moderating effects of anxiety,
age, and coping. Our major hypotheses for Level 1 mod-
erator variables were that: (a) affective differentiation will
become weaker when the number of negative events in-
creases on a given day; and (b) affective differentiation will
become stronger when the number of positive events in-
creases on a given day. Our major hypotheses for Level 2
variables were that affective differentiation will be stronger
in patients with individual difference scores indicative of
greater ‘‘adjustment,’’ specifically: (c) lower anxiety and
(d) and older age. We also predicted that affective differ-
entiation will be stronger in patients who use the following
coping strategies to deal with their breast cancer (e–h):
active coping, planning, positive reframing, and accep-
tance. Finally, we predicted that affective differentiation
will be weaker in patients who use (i-k): denial, behavioral
disengagement, and self-distraction (a form of avoidance)
to cope with their cancer.
Participants were 53 breast cancer patients from the Breast
Center and Helen F. Graham Cancer Center at the Chris-
tiana Care Health System (Newark, Delaware). Inclusion
criteria were a diagnosis of breast cancer (any stage),
surgery within the past 7 months, and ability to speak and
read English. Exclusion criteria were lack of Internet
access at home. Seven participants were removed from the
original sample of 60 because of excessive missing data or
questionable study compliance. Fifty additional partici-
pants originally agreed to participate in the study, but did
not return the informed consent or questionnaire packets.
J Behav Med (2010) 33:441–453 443
Ten patients were screened out due to lack of Internet ac-
cess. Forty-one patients declined to participate. Thus, of the
161 eligible patients, 53 (33%) agreed to participate and
completed the study. This participation rate is comparable
to that obtained in other studies of breast cancer patients
(e.g., 37%: Bellizzi and Blank 2006; 24%: Hinnen et al.
The mean age of the participants was 53.34 years
(SD = 9.99; range = 31–70). Ninety-four percent of the
participants (n = 50) were Caucasian, 4% (n = 2) were
Black, and 2% (n = 1) were Hispanic. Forty-five percent
(n = 24) completed high school, while 36% (n = 19) had
a bachelors degree, 17% (n = 9) had an advanced degree,
and 2% (n = 1) did not answer. Eleven percent (n = 6)
reported a household income between $10,000 and
$40,000, 47% (n = 25) reported an income between
$40,001 and $100,000, 38% (n = 20) reported an income
over $100,000, and 4% (n = 2) did not report income.
Forty-three percent of the participants (n = 23) were
working full-time, 17% (n = 9) were working part-time,
and 40% (n = 21) were not working. Eighty-nine percent
of the women (n = 47) were married, and 11% (n = 6)
Eighty-three percent of the patients (n = 44) had
lumpectomies, 15% (n = 8) had mastectomies, and 2%
(n = 1) had a biopsy only. Twenty-one percent of the
patients (n = 11) were Stage 0 (noninvasive), 53%
(n = 28) were Stage 1 (invasive cancer up to 2 cm with no
lymph node involvement), 21% (n = 11) were Stage 2
(invasive cancer up to 5 cm with no lymph node involve-
ment or greater than 2 cm with lymph node involvement),
4% (n = 2) were Stage 3 (invasive cancer that has spread
to auxiliary lymph nodes that are clumping together, or that
has spread to chest wall, skin of breast, or lymph nodes
near the breastbone or collarbone), and 2% (n = 1) were
Stage 4 (invasive cancer which has spread to other organs
of the body). The mean time since surgery was 42 days
(SD = 49.09). Twenty-three percent of the patients
(n = 12) were receiving radiation and 21% (n = 11) were
receiving chemotherapy during their participation in the
Participants completed questionnaires prior to their par-
ticipation in the daily diary component of the study. The
specific questionnaires are described below.
The 14-item Hospital Anxiety and Depression Scale (Zig-
mond and Snaith 1983) has seven items that assess anxiety
in a medical population, and was normed using medical
patients. A strength of this measure for use with medical
patients is that it does not include somatic symptoms,
preventing an overlap between medical and psychological
symptoms. Participants rated items on a 4-point Likert-type
scale ranging from 0 to 3, with a higher score indicating
more of the symptom described. The reliability and validity
of this measure have been demonstrated (Zigmond and
Snaith 1983), as has the stability of the factor structure
indicating separate anxiety and depression scales in cancer
patients (Moorey et al. 1991). In the current study, the
Cronbach’s alpha for Anxiety was .87.
Participants completed the 28-item Brief COPE question-
naire (Carver 1997) to indicate their past-week use of
various coping strategies to cope with their cancer. The
Brief COPE consists of 14 subscales with two items per
subscale: (a) Self-Distraction; (b) Active Coping; (c)
Denial; (d) Substance Use; (e) Use of Emotional Support;
(f) Behavioral Disengagement; (g) Venting; (h) Use of
Instrumental Support; (i) Positive Reframing; (j) Self-
Blame; (k) Planning; (l) Humor; (m) Acceptance; and (n)
Religion. Participants rated their use of each strategy on a
4-point Likert-type scale ranging from 1 (not at all) to 4 (a
lot). Carver (1997) documented the reliability and validity
of the Brief COPE. In addition, we added one item to
assess ‘‘Seeking Medical Advice’’. This item was worded
‘‘I’ve been seeking advice from my medical team,’’ and
relied on the same Likert-type scale used for the Brief
COPE items. In the current study, the Cronbach’s alphas
for each two-item scale were Self-Distraction = .43,
Active Coping = .75, Denial = .75, Substance Use = .49,
Use of Emotional Support = .87, Behavioral Disengage-
ment = .58, Venting = .45, Use of Instrumental Sup-
port = .76, Positive Reframing = .46, Self-Blame = .80,
Planning = .49, Humor = .90, Acceptance = .67, and
Religion = .91.
Daily diary measures
Participants completed the diary measures for seven con-
secutive nights approximately one week after their com-
pletion of the initial questionnaire packet. The diary took
approximately eight minutes to complete each night. On
average, participants completed 6.8 (out of 7) days of
diaries (SD = .40). This diary completion rate is superior
to that obtained by Badr et al. (2006) in their electronic
diary study of breast cancer survivors. The diary items are
444J Behav Med (2010) 33:441–453
We used the Positive and Negative Affect Schedule—
Expanded Form (PANAS-X; Watson and Clark 1994) to
assess current (state) positive affect and negative affect.
For the purposes of this study, we administered seven items
with negative valence (i.e., sad, lonely, blue, angry, afraid,
scared, frightened) and five items with positive valence
(i.e., enthusiastic, excited, determined, interested, in-
spired). We chose these PANAS items to reflect a broad
range of negative and positive emotions. Participants rated
each item on a 5-point Likert-type scale ranging from 1
(very slightly or not at all) to 5 (extremely) based on how
they felt ‘‘at this moment’’. The PANAS-X is a widely used
measure of affect with convergent and discriminant valid-
ity (Watson and Clark 1994). In this study, between-person
reliabilities were calculated after aggregating each partic-
ipant’s items across all seven days. The between-person
Cronbach’s alphas for negative affect and positive affect
were .91 and .95, respectively. Within-person reliability
estimates were calculated by first transforming item scores
into z-scores within each participant. The within-person
Cronbach’s alphas for negative affect and positive affect
were .78 and .79, respectively.
Participants then completed a 17-item negative events
checklist. Participants indicated whether or not each event
occurred that day. Items included daily work (e.g., too
much work to do) and relationship hassles (e.g., argument
or conflict with my spouse), as well as cancer-specific
stressors (e.g., saw self or scars in mirror; noticed hair
Participants also completed an 11-item positive events
checklist, again indicating whether or not each event
occurred that day. Items included positive daily work (e.g.,
positive event at work) and relationship events (e.g., my
spouse/partner and I had a good laugh together), as well as
cancer-specific positive events (e.g., had a positive inter-
action with a medical professional).
The cancer-specific negative and positive events were
chosen after consultation with staff at the Helen F. Graham
Cancer Center. The other events were based in part on
previous daily event checklists used with adults (e.g.,
Bolger et al. 1989; Cohen et al. 2008).
Patients were identified for potential participation in the
study by breast surgeons’ and oncologists’ staff, via breast
cancer multidisciplinary center appointments, and by
positive breast biopsy lists. After identification for the
study, participants were contacted in person or by phone as
soon as possible after surgery, were screened for study
inclusion, and were invited to participate in the study. If the
participant agreed, we gave or mailed her a questionnaire
packet with instructions to complete the informed consent
and questionnaires and mail them back to the research team
as soon as possible.
Upon receiving the completed questionnaire packet, the
investigator then called the participant to schedule a start
date at the participant’s earliest convenience for the Inter-
net-based daily diaries. The diaries were to be completed
each evening between 7 p.m. and 11 p.m. for seven con-
secutive nights. Entries completed between 4 p.m. and 1:30
a.m. were considered acceptable, to accommodate the fact
that many women did not feel well and had varying work
schedules and responsibilities. Of the 361 valid responses,
83.1% (n = 300) were completed between 7 p.m. and 11
p.m. The average time of diary completion was approxi-
mately 8:30 p.m. The study was conducted in compliance
with the Institutional Review Board of Christiana Care.
Overview of analyses
Our main analyses focused on the influence of daily neg-
ative and positive events, anxiety, age, and coping on
affective differentiation. Specifically, we examined the
moderating effects of these aforementioned variables on
the daily relationship between positive affect and negative
affect. Our data had a two-level structure: Level 1 is the
within-subject level (daily observations over several days
of negative and positive events and negative affect and
positive affect) and Level 2 is the between-subject level
(individual differences on anxiety, age, and coping). To
perform these analyses we used Hierarchical Linear Mod-
eling (Raudenbush and Bryk 2002), which accommodates
missing repeated data when data are missing at random, by
using full information maximum likelihood for parameter
estimation (Schafer and Graham 2002).
The Level 1 (within-subject) regression equation for the
relationship between positive affect (PA) and negative af-
fect (NA) is:
NAt¼ b0þ b1PAt
ð Þ þ et
where NAtis NA at the end of day t, PAtis PA at the end of
day t, b0is the intercept representing the level of NA at
average levels of PA for that individual (centered within
cluster), b1is the slope coefficient for PA (that is, the
number of units higher the NA score is for every additional
unit higher the PA score is on day t), and etis the error
term, or random component of NA on day t. Using Hier-
archical Linear Modeling, this regression equation was
J Behav Med (2010) 33:441–453 445
estimated for each participant. In these analyses, individ-
uals with a more negative slope coefficient for positive
affect (b1) experienced weaker affective differentiation.
Level 1 moderation of affective differentiation
We evaluated two Level 1 moderators of daily affective
differentiation: number of daily negative events and number
of daily positive events. For illustrative purposes, we pres-
ent the equation for the Level 1 regression equation for the
relationship between daily affect and daily negative events:
NAt¼ b0þ b1PAt
ð Þ þ b2NEt
ðÞ þ b3PAt? NEt
ð Þ þ et
where NAtis negative affect at the end of day t, PAtis the
positive affect at the end of day t, NEtis the number of
negative events on day t, PAt9 NEtis the interaction
between positive affect and negative events on day t, b0is
the intercept representing the level of negative affect at
average levels of positive affect, number of negative
events, and the interaction between positive affect (cen-
tered within cluster) and negative events (centered within
cluster) for that individual, b1is the slope coefficient for
positive affect (that is, the number of units higher the
negative affect score is for every additional unit higher the
positive affect score is on day t), b2is the slope coefficient
for negative events (that is, the number of units higher the
negative affect score is for every additional negative event
on day t), b3is the slope coefficient for the positive affect
by negative events interaction term (that is, the number of
units higher the negative affect score is for every additional
unit higher the positive affect by negative events interac-
tion term is on day t), and etis the error term, or random
component of negative affect on day t. Using Hierarchical
Linear Modeling, this regression equation was estimated
for each participant. In these analyses, individuals with a
higher (more negative) slope coefficient for the positive
affect by negative event interaction term (b3), experienced
greater change in their affective differentiation when neg-
ative events increased.
Level 2 moderation of affective differentiation
The second-level (between-subject) equations used level-2
variables to predict between-subjects differences in the
Level 1 slopes. For illustrative purposes, the equation be-
low is the second-level slope equation for the moderating
effect of age, controlling for anxiety (from the Hospital
Anxiety and Depression Scale). (See later section for an
explanation of the statistical control of anxiety). The sec-
ond-level (between-subject) equation using age and anxiety
to predict the slope coefficient for person j (predicting each
individual’s within-subject slope as the outcome), is:
b1j¼ c10þ c11Agej
where each individual’s slope coefficients for positive af-
fect, b1, is predicted by an intercept, c10, an age coefficient,
c11, an anxiety coefficient, c12, and random error, uj. Each
Level 2 variable was grand mean centered, meaning that
the intercepts, c10, represent the predicted slope coefficient
for the average person. The age coefficient, c11, and the
anxiety coefficient, c12, indicate the change in affective
differentiation slope as a function of individual differences
in age and anxiety.
Although not the focus of our analyses, we also evalu-
ated between-subject differences in the Level 1 intercept.
The second-level (between-subject) equation using age and
anxiety to predict the intercept for person j (predicting each
individual’s intercept as the outcome) is:
b0j¼ c00þ c01Agej
where each person’s intercept (her end-of-day negative
affect at an average level of positive affect), b0, is predicted
by a second level intercept, c00, an age coefficient, c01, an
anxiety coefficient, c02, and random error, uj. Each Level 2
variable was grand mean centered, meaning that the second
level intercept, c00, represents end-of-day negative affect
for the average person. The age coefficient, c01, and the
anxiety coefficient, c02, indicate the change in end-of-day
negative affect as a function of individual differences in
age and anxiety.
Participants’ scores on the anxiety (M = 5.91, SD = 4.06)
scale were consistent with those obtained in other studies of
breast cancer patients (e.g., Hinnen et al. 2008). On average,
patients’ anxiety scores were well below the clinical cutoff.
On average, patients reported 2.59 (SD = 1.74) negative
events each day, and 5.58 (SD = 2.36) positive events each
day. The two most frequently occurring negative events
were ‘‘saw self or scars in mirror,’’ and ‘‘fatigue.’’ The two
most frequently occurring positive events were ‘‘felt phys-
ically okay today,’’ and ‘‘got out and did something today
that felt good.’’
Based on Muthen and Muthen (2002), we used a Monte
Carlo simulation for the Level 1 relationship between
number of daily negative events and affective differentia-
446J Behav Med (2010) 33:441–453
tion, a finding previously reported by Zautra et al. (2005a),
and estimated that our level of power was .57 for p\.05.
Table 1 lists the within-person correlations, which repre-
sent the relationships between the within-group deviations
of each variable (Snijders and Boskers 1999). Table 2
presents the correlations between the major study variables
and anxiety and age. (See a later section for an explanation
of the importance of anxiety.) Anxiety was significantly
and positively correlated with self-distraction, denial,
behavioral disengagement, venting, self-blame, planning,
negative affect, and negative events, and was significantly
and negatively correlated with age. Age was significantly
and positively correlated with acceptance, and was signif-
icantly and negatively correlated with anxiety, behavioral
disengagement, negative affect, and negative events.
Affective differentiation results
We compared the model fit for the unrestricted, homoge-
neous sigma-squared, and first-order autoregressive Hier-
archical Linear Modeling models. The unrestricted model,
with unrestricted within-person and between-person vari-
ation, had a significantly better fit than the other two
models. Therefore, the findings presented below come from
models assuming unrestricted error variances/covariances.
The overall slope for positive affect predicting negative
affect was significant, c10= -.136, p = .004. We next
examined variables that might influence (moderate) the
relationship between positive affect and negative affect and
that might predict a stronger or weaker relationship be-
tween the two (see Table 3).
At Level 1, we examined the number of daily events as a
potential moderator of affective differentiation by evalu-
ating the Level 1 interaction between events and positive
affect in the prediction of negative affect. Contrary to our
hypotheses, positive events did not significantly moderate
Table 1 Within-person correlations for the daily variables
1. Positive affect-.19-.05.26
2. Negative affect.18-.21
3. # Negative events-.01
4. # Positive events
These within-person correlations represent the relationships between
the within-group deviations of each variable (Snijders and Boskers
Table 2 Correlations between anxiety and age and the major study
Active coping .17 .18
Emotional support .05.08
Medical advice .16.10
Days since surgery-.20-.26
Positive affect.14 .08
* p\.05, ** p\.01
Table 3 Multilevel regressions: effects of moderators on affective
Positive events, c30
Negative events, c30
Active coping, c11
Positive reframing, c11
Behavioral disengagement, c11
Seeking medical advice, c11
Substance use, c11
Emotional support, c11
Instrumental support, c11
Note At level 2, we controlled for anxiety scores
* p\.10, ** p\.05, *** p\.01
J Behav Med (2010) 33:441–453447
affective differentiation, c30= -.008, p = .779, nor did
negative events, c30= .035, p = .388. These results re-
mained the same even without the statistical control of
At Level 2, each variable formed a cross-level interac-
tion between the Level 2 moderating variable and Level 1
positive affect in the prediction of negative affect. Anxiety
had a significant moderating effect on the slope between
positive affect and negative affect, c11= -.058, p\.001.
Specifically, as expected, increased anxiety was associated
with a stronger daily relationship between positive affect
and negative affect, that is, weaker affective differentia-
tion. Because anxiety was significantly correlated with age
and many of the coping strategies (see Table 2), and had a
significant moderating effect on affective differentiation,
all subsequent multilevel analyses were conducted con-
trolling for anxiety to address anxiety’s role as a potential
Moderators of affective differentiation
All results reported below were obtained after controlling
for anxiety. As predicted, age had a significant moderating
effect on affective differentiation, c11= .018, p\.001,
with older patients evidencing stronger affective differen-
acceptance coping (trend), c11= .067, p = .082, also
moderated the slope between positive affect and negative
affect, with increased planning and acceptance associated
with stronger affective differentiation.
We also hypothesized that active coping and positive
reframing would be associated with stronger affective
differentiation, but these variables were not significant
We hypothesized that both behavioral disengagement
and denial coping would be associated with weaker
affective differentiation. Behavioral disengagement coping
had a significant moderating effect on the slope between
positive affect and negative affect, c11= -.367, p\.001,
as did denial coping, c11= -.269, p\.001. As predicted,
increased use of both coping strategies was associated with
a stronger daily relationship between positive affect and
negative affect, that is, weaker affective differentiation. We
also hypothesized that self-distraction would be associated
with weaker affective differentiation, but this coping
strategy was not a significant moderator (p = .963).
For exploratory purposes, we evaluated the moderating
role of the other coping strategies that we measured prior to
the diary portion of the study (i.e., substance use, use of
emotional support, venting, use of instrumental support,
self-blame, humor, religion, and seeking medical advice).
The following coping strategies had a significant moder-
p = .001, and
ating effect on the slope between positive affect and neg-
ative affect, with increased use associated with a weaker
daily relationship between positive affect and negative
affect (stronger affective differentiation): (a) substance use,
c11= .177, p = .018; (b) religion, c11= .057, p = .009;
and (c) seeking medical advice, c11= .249, p\.001. On
the other hand, humor had an opposite moderating effect
on the slope between positive affect and negative affect,
c11= -.058, p = .010, with increased use of humor
associated with a stronger daily relationship between po-
sitive affect and negative affect, that is, weaker affective
differentiation. Use of emotional support, instrumental
support, venting, and self-blame were not significant
moderators of affective differentiation (p’s[.13).1,2,3,45
1We repeated all analyses with exact time of diary completion as a
level 1 control variable, but its inclusion did not affect the results. In
addition, we dummy coded time of diary completion, with 4 p.m.–
6.59 p.m. as the reference time period, and 7.00 p.m.–7.59 p.m.; 8.00
p.m.–9.59 p.m.; and 10.00 p.m.–2.00 a.m. as the other time periods.
We then repeated all analyses with statistical control of these three
dummy coded level 1 variables (time periods). All results remained
the same, with one exception: The coping strategy of Acceptance
emerged as a significant moderator of affective differentiation,
whereas originally it was a near-significant moderator.
2We also conducted analyses with each person’s average positive
affect score as a level 2 control variable, but its inclusion did not
affect the results.
3Patients’ treatment status (yes/no for radiation or chemotherapy)
during their participation in the study was a significant moderator of
affective differentiation, c11= - .368, p\.001, such that those who
were receiving treatment had weaker affective differentiation than
those who were not. This pattern was obtained even with the statis-
tical control of anxiety, c11= -.319, p = .002. Educational back-
ground, working status, and number of days since surgery were also
examined as potential moderators, but none of these variables sig-
nificantly moderated affective differentiation, with or without anxiety
as a control.
4Without controlling for initial anxiety, age, planning, behavioral
disengagement, denial, humor, and seeking medical advice continued
to serve as significant moderators of affective differentiation, and
acceptance was a significant (rather than near-significant) moderator.
Religion became a non-significant moderator and substance use was a
near-significant moderator (p\.10). When both anxiety and treat-
ment status (yes/no for radiation or chemotherapy) were included in
the models, age, planning, behavioral disengagement, denial, humor,
religion, substance use, and seeking medical advice were all signifi-
cant moderators of affective differentiation, and acceptance was a
near-significant moderator of affective differentiation.
5For exploratory purposes, we also tested the moderating effects of a
number of initial individual difference variables not reported in the
text, specifically, neuroticism and extraversion (both measured with
the NEO Five Factor Inventory; Costa and McCrae 1992), depression
(with the Hospital Anxiety and Depression Scale; Zigmond and
Snaith 1983), and mood clarity (with the Mood Clarity Subscale of
the Trait Meta-Mood Scale; Salovey et al. 1995). Depression,
c11= - .053, p = .001, and neuroticism, c11= - .026, p\.001,
were significant moderators of affective differentiation, with higher
scores on both variables associated with weaker affective differenti-
ation. Extraversion was also a significant moderator of affective dif-
ferentiation, c11= .015, p = .031, with higher extraversion scores
448J Behav Med (2010) 33:441–453
We examined the correlations between the eight coping
types that significantly or near-significantly moderated
affective differentiation (planning, acceptance, behavioral
disengagement, denial, substance use, humor, religion, and
seeking medical advice), to evaluate whether their effects
reflected strong inter-relations, that is, redundancy among
them. There were only three significant correlations and all
were modest in magnitude. Planning was significantly
positively correlated with Seeking Medical Advice,
r = .36, p = .01. Denial was significantly positively cor-
related with Behavioral Disengagement, r = .33, p = .02.
Humor was significantly positively related to Substance
Use, r = .36, p = .01.
Influenced by the Dynamic Model of Affect, we used a
daily diary methodology to examine potential Level 1 (day-
level) and Level 2 (person-level) moderators of the rela-
tionship between breast cancer patients’ daily negative
affect and positive affect. Specifically, we examined the
moderating effects of daily negative and positive events,
anxiety, age, and coping on daily affective differentiation.
At the outset, we would like to highlight some of the
study’s methodological strengths. Like Badr et al. (2006),
we used an electronic assessment procedure, which
allowed us to document that patients completed their dia-
ries every night, as required. We measured breast cancer
patients’ daily affect soon after their surgery, in hopes of
capturing a stressful period of their illness trajectory
(Hinnen et al. 2008). In contrast, Badr et al. (2006) mea-
sured patients’ daily affect several years after their surgery.
Finally, we sampled 53 patients, whereas Badr et al.
It should be noted that, overall, daily negative affect and
positive affect were significantly inversely related. Patients
completed daily diaries approximately one month (42 days
on average) after their surgery, a time thought to be
somewhat stressful (Hinnen et al. 2008). Their daily reports
of positive affect and negative affect evidenced weak
affective differentiation, as would be expected by the
Dynamic Model of Affect. This finding is consistent with
the inverse relationship between negative affect and posi-
tive affect found in Zautra et al.’s (2001) studies with
arthritis and fibromyalgia patients.
Daily events and affective differentiation
We predicted that daily events would moderate affective
differentiation, such that as the number of negative events
increased, affective differentiation would weaken, and as
the number of positive events increased, affective differ-
entiation would strengthen. However, neither prediction
was supported, regardless of whether anxiety was in the
equation. These findings are inconsistent with those of
Zautra et al. (2005a), who found that, for individuals with
rheumatoid arthritis, number of positive events was asso-
ciated with stronger affective differentiation, and that
number of negative events was associated with weaker
affective differentiation. The difference in our results could
be due to a number of factors. First, there was a difference
in sample size, with Zautra et al. (2005a) having a sample
size of 93 compared to our sample size of 53. Also, Zautra
et al.’s (2005a) participants completed diaries for 30 days,
whereas our patients completed diaries for seven days.
Finally, Zautra et al. (2005a) studied individuals with
rheumatoid arthritis, who may have different daily expe-
riences and emotional reactions than the breast cancer
patients in our study.
As we reported previously, using a Monte Carlo simu-
lation, we estimated that our level of power was .57 to
detect the moderating effect of number of daily negative
events on daily affective differentiation (Muthen and
Muthen 2002). Obviously, our evaluation of the moderating
effects of daily events on cancer patients’ affective differ-
entiation was under-powered. We also estimated that if we
had sampled 100 participants (for 7 days), rather than our
current 53 participants, our power would have increased to
.78. Obviously, this type of research, specifically the eval-
uation of the effects of daily events on affective differen-
tiation, requires a large sample of cancer patients.
Person-level moderators of affective differentiation
As hypothesized, anxiety was associated with weaker
affective differentiation. This finding is consistent with the
results of Ong and Bergeman (2004), who found that in
older adults, higher perceived stress and higher levels of
neuroticism were associated with weaker affective differ-
entiation (see footnote 4). Because anxiety was also sig-
nificantly correlated with several of the other variables in
our study (see Table 2), all further multilevel analyses were
conducted controlling for anxiety.
Age and affective differentiation
Many studies have found that younger patients have a more
difficult time adjusting to breast cancer (e.g., Turner et al.
Footnote 5 continued
associated with stronger affective differentiation. Mood clarity was
not a significant moderator of affective differentiation, c11= .060,
p = .414.
J Behav Med (2010) 33:441–453 449
2005). As we reported previously, in our sample, patients’
age was negatively correlated with their anxiety scores. We
predicted that older patients would have stronger affective
differentiation, and our results supported this hypothesis.
However, research has shown that age is related to emotion
regulation in non-cancer populations as well, with older
adults demonstrating greater emotion regulation skills than
younger adults (e.g., Gross et al. 1997; Labouvie-Vief et al.
1989). It is therefore possible that even with a nonclinical
sample of adults, affective differentiation would be greater
in the older compared to the younger participants. Future
research is needed to evaluate affective differentiation
across the lifespan.
Coping and affective differentiation
We predicted that active coping, planning, positive refra-
ming, and acceptance coping would be associated with
stronger affective differentiation, and that behavioral
disengagement, self-distraction, and denial would be
associated with weaker affective differentiation. These
predictions were based on previous research with cancer
patients, discussed previously, that demonstrated the
adaptive value of the former coping strategies and the
maladaptive value of the latter coping strategies.
Our results supported our hypotheses for planning,
acceptance (trend), behavioral disengagement, and denial.
Exploratory analyses of the other coping strategies showed
that substance use, religious coping, and seeking medical
advice were associated with stronger affective differentia-
tion, whereas use of humor was associated with weaker
In general, the pattern of these coping findings supports
the adaptive value of affective differentiation: Most of the
coping strategies that were associated with stronger affec-
tive differentiation have been shown to be helpful for
cancer patients, and most of the strategies that were asso-
ciated with weaker affective differentiation have been
shown to be detrimental. It is important to emphasize that
these findings were obtained with the statistical control of
patients’ anxiety, and thus are not merely redundant with
concurrent distress. Moreover, the interrelations among
these coping strategies were modest, suggesting that their
moderating effects on affective differentiation were unique
and not redundant.
Specifically, we previously noted the beneficial effects
of planning (e.g., Karanci and Erkam 2007) and acceptance
(e.g., Roussi et al. 2007), as well as the deleterious effects
of behavioral disengagement (Compas et al. 2006) and
denial (Roussi et al. 2007). Seeking medical advice was
associated with stronger affective differentiation. This type
of coping is an active strategy, and its positive effect on
affective differentiation is consistent with the literature
showing the adaptive value of active forms of coping with
cancer (e.g., Bellizzi and Blank 2006). Use of religious
coping was also associated with stronger affective differ-
entiation. Although we did not hypothesize this finding, it
is quite consistent with some of the literature on coping
with cancer, which has suggested the beneficial effects of
religious coping and attempts at meaning-making more
broadly (e.g., Park 2010; Urcuyo et al. 2005).
On the other hand, two of the coping findings were
surprising, specifically substance use’s positive effect on
affective differentiation, and humor’s negative effect on
affective differentiation. The substance use scale of the
Brief COPE has two items: ‘‘I’ve been using alcohol or
other drugs to make myself feel better,’’ and ‘‘I’ve been
using alcohol or other drugs to help me get through it.’’
Given that our study was conducted relatively soon after
patients’ surgery (42 days later, on average), and that 42%
of our patients were receiving treatment (radiation or
chemotherapy) during their participation in the study, we
suspect that the ‘‘substance use’’ finding reflects the ben-
eficial effects of medication for pain and associated sur-
gery- and treatment-related symptoms. However, we are
not sure how to interpret humor’s negative effect on
affective differentiation, given that, in general, the research
literature has supported its value in coping with cancer
(e.g., Carver et al. 1993).
We next consider the potential mechanisms responsible
for the coping strategies’ influence on daily affective dif-
ferentiation. This discussion is speculative, given that we
did not measure coping on a daily basis and did not
measure potential mediators of the coping-affective dif-
ferentiation relationship (e.g., appraisals). That said, it is
possible that the active forms of coping, specifically
planning and seeking medical advice, promoted feelings of
control and perceived self-efficacy, which increased posi-
tive affect, although not necessarily negative affect. Active
coping’s effect on negative affect might be minimal
because this type of coping is hard work and forces the person
to face difficult situations. Our interpretation is consistent
with research by Felton and Revenson (1984), Zautra et al.
(1995), and Folkman (1997), who found that active forms of
coping increase positive affect but do not reduce negative
affect, a pattern which would promote the independence of
the two affects and thus affective differentiation. Thus,
teaching breast cancer patients to use active coping strategies
might have beneficial effects on their daily lives, specifically
by facilitating the independence of their daily negative affect
and positive affect and thus allowing them to experience the
latter even in the face of some distress.
Behavioral disengagement, or passively giving up
attempts to cope with the cancer, and denial also moderated
daily affective differentiation, with greater use of both
450J Behav Med (2010) 33:441–453
strategies associated with weaker affective differentiation.
Behaviorally disengaging from their illness and treatment,
and denying aspects of their cancer experience, probably
hindered patients’ feelings of control, precluding their
ability to experience daily positive affect in the face of
daily negative affect.
It is unclear why religious coping was associated with
stronger affective differentiation. Perhaps like active cop-
ing, it promoted a sense of control and increased positive
affect (but did not significantly decrease negative affect)
(Pargament 1997). As we mentioned previously, the sub-
stance use items probably assessed the patients’ use of pain
medication, which might have strengthened their affective
differentiation by reducing daily negative affect but not
affecting daily positive affect. Finally, humor’s negative
effect on affective differentiation might reflect its use by
some patients in a passive, maladaptive fashion, although
humor’s mostly nonsignificant correlations with the other
coping strategies do not shed light on this issue.
Future research is needed to better understand the role of
coping in cancer patients’ affective differentiation. Our
findings in this regard are quite interesting, for theory on
both coping and the Dynamic Model of Affect, because
they suggest that the use of specific coping strategies
influences the availability of attentional and cognitive
resources needed for daily affective differentiation.
Limitations and future directions
Despite the benefits of our study design, there are several
limitations that should be acknowledged. First, as reported
previously, our study did not have sufficient power to
adequately test the level 1 moderating effects of daily
events. Most of our patients were Caucasian and it is
unknown if the results would generalize to minority
patients. To reduce participant burden, we asked our
patients to complete the daily diaries for just seven days,
once each day at the end of the night. A longer length of
time might have captured events that a week did not.
Moreover, end-of-day assessment of affect and events is
more vulnerable to a retrospective bias compared to a signal-
contingent measurement strategy (Bolger et al. 2003).
Also, we relied on the participants’ self-report of events,
mood, and coping, and we had no way of corroborating
their responses. In addition, coping was measured in a
single-administration questionnaire that was completed
approximately one week before the participants completed
the daily diaries. Therefore, it is not known whether the
types of coping influenced patients’ emotional states, or
whether patients’ emotional states influenced their use of
certain coping strategies.
Because our coping measure used a one-week time
frame, it was vulnerable to a retrospective bias. A better
strategy would have been to measure daily coping as part
of the nightly diaries (Stone et al. 1998). On the other hand,
some researchers have suggested that, for some types of
problems and some types of coping, a one-day window is
too short, because it misses coping efforts that unfold over
several days (e.g., Folkman and Moskowitz 2004). It
should also be noted that the Brief COPE subscales had
only two items each. This explains why some of the scales’
internal reliability was low. The full length version of the
COPE consists of four items per subscale, and thus ensures
better internal reliability.
When evaluating the role of daily stressors (and positive
events) in affective differentiation, we treated each daily
event as equal in meaning and impact; we did not attempt
to differentiate daily events on the basis of subjective or
objective (e.g., judge-based) criteria. Many leading life
events researchers (e.g., Monroe 2008) advocate the use of
objective criteria to assess major life stress, although their
use in research on daily stress (hassles) is infrequent and
often impractical. In addition, although our daily events
checklist was modeled after other checklists used with
adults (Bolger et al. 1989; Cohen et al. 2008), and included
items thought to be relevant to cancer patients, the com-
pleteness of our checklist is unknown.
Zautra et al. (2001, 2005b) measured affect once a week
over 10–20 weeks, whereas we measured affect once each
day in the evening. Affect reported at the end of the day
may not be a true indicator of a participant’s mood at
various points during that day. The duration of the affect
may vary by the person, by what events took place that day,
the time of diary completion, the strength of the affect, or
an untold number of factors. Therefore, we cannot make
conclusions about the relationship between patients’ neg-
ative affect and positive affect during any other time be-
sides the one point of time in which we measured their
affect. In addition, we do not know whether affective dif-
ferentiation is a benefit for our patients. Future studies
should examine whether affective differentiation is related
to later adjustment. Finally, our findings concerning age
and coping require replication.
To our knowledge, our study is the second to use a daily
diary methodology to study the emotional experiences of
breast cancer patients (Badr et al. 2006), and the first to
apply the construct of affective differentiation to a cancer
population. Specifically, we evaluated the moderating role
of several within-subject and between-subject variables.
Our results indicated that age moderated daily affective
differentiation, such that older breast cancer patients had
stronger affective differentiation than younger patients.
J Behav Med (2010) 33:441–453451
Consistent with our hypotheses, greater use of planning and
acceptance coping (trend) to deal with their cancer was
associated with stronger daily affective differentiation,
whereas higher anxiety and greater use of behavioral dis-
engagement and denial coping were associated with
weaker affective differentiation. These findings, combined
with the moderating effects of other coping strategies (e.g.,
seeking medical advice and religious coping), suggest the
adaptive value of affective differentiation, as proposed by
the Dynamic Model of Affect (Reich et al. 2003). Pro-
spective studies are needed to examine whether affective
differentiation is related to later adjustment of breast cancer
American Cancer Society. (2009). Estimated new cases and deaths by
sex, US, 2009. Retrieved October 28, 2009, from http://www.
Badr, H., Basen-Engquist, K., Taylor, C. L. C., & de Moor, C. (2006).
Mood states associated with transitory physical symptoms
among breast and ovarian cancer survivors. Journal of Behav-
ioral Medicine, 29, 461–475.
Bellizzi, K. M., & Blank, T. O. (2006). Predicting posttraumatic
growth in breast cancer survivors. Health Psychology, 25, 47–56.
Bolger, N., Davis, A., & Rafaeli, E. (2003). Dairy methods: Capturing
life as it is lived. Annual Review of Psychology, 54, 579–616.
Bolger, N., DeLongis, A., Kessler, R., & Schilling, E. (1989). Effects
of daily stress on negative mood. Journal of Personality and
Social Psychology, 57, 808–818.
Carver, C. S. (1997). You want to measure coping but your protocol’s
too long: Consider the Brief COPE. International Journal of
Behavioral Medicine, 4, 92–100.
Carver, C., Pozo, C., Harris, S., Noriega, V., Scheier, M., Robinson,
D., et al. (1993). How coping mediates the effect of optimism on
distress: A study of women with early stage breast cancer.
Journal of Personality and Social Psychology, 65, 375–390.
Cohen, L. H., Gunthert, K., Butler, A., Parrish, B., Wenze, S., &
Beck, J. (2008). Negative affective spillover from daily events
predicts early response to cognitive therapy for depression.
Journal of Consulting and Clinical Psychology, 76, 955–965.
Compas, B. E., Beckjord, E., Agocha, B., Sherman, M. L., Langrock,
A., Grossman, C. I., et al. (2006). Measurement of coping and
stress responses in women with breast cancer. Psycho-Oncology,
Costa, P. T., & McCrae, R. R. (1992). The NEO Personality Inventory
(NEO-PI) and NEO Five-Factor Inventory (NEO-FFI) profes-
sional manual. Odessa, FL: Psychological Assessment Re-
Culver, J. L., Arena, P. L., Antoni, M. H., & Carver, C. S. (2002).
Coping and distress among women under treatment for early
stage breast cancer: Comparing African Americans, Hispanics,
and non-Hispanic Whites. Psycho-Oncology, 11, 495–504.
Davis, M. C., Zautra, A. J., & Smith, B. W. (2004). Chronic pain,
stress, and the dynamics of affective differentiation. Journal of
Personality, 72, 1133–1159.
Dunn, J., & Steginga, S. K. (2000). Young women’s experience of
breast cancer: Defining young and identifying concerns. Psycho-
Oncology, 9, 137–146.
Epping-Jordan, J. E., Compas, B. E., Osowiecki, D. M., Oppedisano,
G., Gerhardt, C., Primo, K., et al. (1999). Psychological
adjustment in breast cancer: Processes of emotional distress.
Health Psychology, 18, 315–326.
Felton, B., & Revenson, T. (1984). Coping with chronic illness: A
study of illness controllability and the influence of coping
strategies on psychological adjustment. Journal of Consulting
and Clinical Psychology, 52, 343–353.
Folkman, S. (1997). Positive psychological states and coping with
severe stress. Social Science and Medicine, 45, 1207–1221.
Folkman, S., & Moskowitz, J. (2004). Coping: Pitfalls and promise.
Annual Review of Psychology, 55, 745–774.
Green, B. L., Krupnick, J. L., Rowland, J. H., Epstein, S. A.,
Stockton, P., Spertus, I., et al. (2000). Trauma history as a
predictor of psychologic symptoms in women with breast cancer.
Journal of Clinical Oncology, 18, 1084–1093.
Gross, J. J., Carstensen, L. L., Pasupathi, M., Tsai, J., Gotestam
Skorpen, C., & Hsu, A. Y. C. (1997). Emotion and aging:
Experience, expression, and control. Psychology and Aging, 12,
Hinnen, C., Ranchor, A., Sanderman, R., Snijders, T., Hagedoorn, M.,
& Coyne, J. (2008). Course of distress in breast cancer patients,
their partners, and matched control couples. Annals of Behav-
ioral Medicine, 36, 141–148.
Karademas, E. C., Karvelis, S., & Argyropoulou, K. (2007). Stress-
related predictors of optimism in breast cancer survivors. Stress
and Health: Journal of the International Society for the
Investigation of Stress, 23, 161–168.
Karanci, A. N., & Erkam, A. (2007). Variables related to stress-
related growth among Turkish breast cancer patients. Stress and
Health: Journal of the International Society for the Investigation
of Stress, 23, 315–322.
Labouvie-Vief, G., Hakim-Larson, J., DeVoe, M., & Schoeberlein, S.
(1989). Emotions and self-regulation: A life span view. Human
Development, 32, 279–299.
McCaul, K. D., Sandgren, A. K., King, B., O’Donnell, S., Branstetter,
A., & Foreman, G. (1999). Coping and adjustment to breast
cancer. Psycho-Oncology, 8, 230–236.
Monroe, S. (2008). Modern approaches to conceptualizing and
measuring human stress. Annual Review of Clinical Psychology,
Moorey, S., Greer, S., Watson, M., Gorman, C., Rowden, L.,
Tunmore, R., et al. (1991). The factor structure and factor
stability of the Hospital Anxiety and Depression Scale in patients
with cancer. British Journal of Psychiatry, 158, 255–259.
Muthen, L., & Muthen, B. (2002). How to use Monte Carlo study to
decide on sample size and determine power. Structural Equation
Modeling, 9, 599–620.
Ong, A. D., & Bergeman, C. S. (2004). The complexity of emotions
in later life. Journal of Gerontology: Psychological Sciences,
Pargament, K. (1997). The psychology of religion and coping:
Theory, research, and practice. New York: Guilford.
Park, C. (2010). Making sense of the meaning literature: An
integrative review of meaning making and its effects of
adjustment to stressful life events. Psychological Bulletin, 136,
Potter, P., Zautra, A., & Reich, J. (2000). Stressful events and
information processing dispositions moderate the relationship
between positive and negative affect: Implications for pain
patients. Annals of Behavioral Medicine, 22, 191–198.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear
models: Applications and data analysis methods (2nd ed.).
Thousand Oaks, CA: Sage.
Reddick, B. K., Nanda, J. P., Campbell, L., Ryman, D. G., & Gaston-
Johansson, F. (2005). Examining the influence of coping with
pain on depression, anxiety, and fatigue among women with
breast cancer. Journal of Psychosocial Oncology, 23, 137–157.
452J Behav Med (2010) 33:441–453
Reich, J. W., Zautra, A. J., & Davis, M. (2003). Dimensions of affect
relationships: Models and their integrative implications. Review
of General Psychology, 7, 66–83.
Roussi, P., Krikeli, V., Hatzidimitriou, C., & Koutri, I. (2007). Patterns
of coping and psychological distress in women diagnosed with
breast cancer. Cognitive Therapy and Research, 31, 97–109.
Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P.
(1995). Emotional attention, clarity, and repair: Exploring
emotional intelligence using the Trait Meta-Mood Scale. In J.
W. Pennebaker (Ed.), Emotion, disclosure, and health (pp. 125–
134). Washington, DC: American Psychological Association.
Schafer, J., & Graham, J. (2002). Missing data: Our view of the state
of the art. Psychological Methods, 7, 147–177.
Sears, S. R., Stanton, A. L., & Danoff-Burg, S. (2003). The yellow
brick road and the emerald city: Benefit finding, positive
reappraisal coping, and posttraumatic growth in women with
early-stage breast cancer. Health Psychology, 22, 487–497.
Snijders, T. A. B., & Boskers, R. J. (1999). Multilevel analysis: An
introduction to basic and advanced multilevel modeling. Lon-
Stanton, A. L., Danoff-Burg, S., Cameron, C. L., Bishop, M., Collins,
C. A., Kirk, S. B., et al. (2000). Emotionally expressive coping
predicts psychological and physical adjustment to breast cancer.
Journal of Consulting and Clinical Psychology, 68, 875–882.
Stanton, A. L., Danoff-Burg, S., & Huggins, M. E. (2002). The first
year after breast cancer diagnosis: Hope and coping strategies as
predictors of adjustment. Psycho-Oncology. Special Issue:
Survivorship, 11, 93–102.
Stanton, A. L., & Snider, P. R. (1993). Coping with a breast cancer
diagnosis: A prospective study. Health Psychology, 12, 16–23.
Stone, A., Schwartz, J., Neale, J., Shiffman, S., Marco, C., Hickcox,
M., et al. (1998). A comparison of coping assessed by ecological
momentary assessment and retrospective recall. Journal of
Personality and Social Psychology, 74, 1670–1680.
Turner, J., Kelly, B., Swanson, C., Allison, R., & Wetzig, N. (2005).
Psychosocial impact of newly diagnosed advanced breast cancer.
Psycho-oncology, 14, 396–407.
Urcuyo, K., Boyers, A., Carver, C., & Antoni, M. (2005). Finding
benefit in breast cancer: Relations with personality, coping, and
concurrent well-being. Psychology and Health, 20, 175–192.
Watson, D., & Clark, L. A. (1994). The PANAS-X: Manual for the
positive and negative affect schedule- expanded form. Iowa City:
University of Iowa.
Zautra, A. J., Affleck, G. G., Tennen, H., Reich, J. W., & Davis, M. C.
(2005a). Dynamic approaches to emotions and stress in everyday
life: Bolger and Zuckerman reloaded with positive as well as
negative affects. Journal of Personality, 73, 1511–1538.
Zautra, A., Burleson, M., Smith, C., Blalock, S., Wallston, K.,
DeVellis, R., et al. (1995). Arthritis and perceptions of quality of
life: An examination of positive and negative affect in rheuma-
toid arthritis patients. Health Psychology, 14, 399–408.
Zautra, A. J., Johnson, L. M., & Davis, M. C. (2005b). Positive affect
as a source of resilience for women in chronic pain. Journal of
Consulting and Clinical Psychology, 73, 212–220.
Zautra, A., Smith, B., Affleck, G., & Tennen, H. (2001). Examina-
tions of chronic pain and affect relationships: Applications of a
dynamic model of affect. Journal of Consulting and Clinical
Psychology, 69, 786–795.
Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and
depression scale. Acta Psychiatrica Scandinavica, 67, 361–370.
J Behav Med (2010) 33:441–453453