Health Psychology
2004, Vol. 23, No. 1, 3–15
Copyright 2004 by the American Psychological Association, Inc.
0278-6133/04/$12.00 DOI: 10.1037/0278-6133.23.1.3
Psychological and Physical Adjustment to Breast Cancer Over 4 Years:
Identifying Distinct Trajectories of Change
Vicki S. Helgeson, Pamela Snyder, and Howard Seltman
Carnegie Mellon University
The goal of this study was to identify distinct trajectories of adjustment to breast cancer over 4 years as
well as to distinguish among the different trajectories. The mental and physical functioning of 287
women with breast cancer who remained alive and disease free through 4 years of follow-up were
examined. The majority of women showed slight and steady improvement in functioning with time, but
subgroups of women were identified who showed marked improvement and marked deteriorations over
time. Age successfully distinguished different trajectories of physical functioning. Indices of personal
resources (i.e., self-image, optimism, perceived control) and social resources (i.e., social support)
successfully distinguished different courses of mental and physical functioning.
Key words: adjustment to breast cancer, quality of life, personal resources, social resources
lower distress (e.g., during chemotherapy) and higher distress
(e.g., during relapse; Heim, Valach, & Schaffner, 1997). However,
none of these studies has identified distinct patterns of adjustment
over time.
The present study attempts to fill this gap by examining patterns
of adjustment to breast cancer over a 4-year period. It is expected
that the majority of people will respond to the diagnosis and
treatment for breast cancer with some distress. The question
is— do some people remain distressed for long periods of time
whereas others regain some normalcy in their lives after a period
of time? Thus, the first goal of the study is to identify distinct
trajectories of adjustment to breast cancer over 4 years. The second
goal is to determine whether we can distinguish among people who
follow different trajectories. We especially wanted to distinguish
between those who remain distressed and those who rebound to
more healthy functioning.
What factors might differentiate groups of women who show
distinct trajectories of mental and physical functioning? According
to stress and coping theory, the experience of stress, and presumably psychological distress, results when demands exceed resources (Lazarus & Folkman, 1984). Two major sets of resources
have been discussed: internal or personal (Taylor, 1983; Taylor &
Brown, 1988) and external or social (Cohen & Wills, 1985; Helgeson & Cohen, 1996). We examine the implications that these two
sets of resources might have for women’s trajectories of psychological and physical functioning over 4 years following the diagnosis of breast cancer. Although previous research has not examined the implications of personal and social resources for
trajectories of functioning, research has examined their implications for adjustment at a single point in time. Thus, we briefly
review these literatures.
A great deal of research in the area of chronic illness, including
cancer, has focused on how people adjust to disease. Most of these
studies, however, have focused on the initial adjustment period. In
the area of cancer, the majority of people now survive the disease,
with survival defined as being disease free for 5 years (American
Cancer Society, 2002). Women with breast cancer are one of the
largest groups of cancer survivors. Most breast cancers are diagnosed at an early stage, and 96% of those with localized disease
and 78% of those with regional disease survive 5 years (American
Cancer Society, 2002).
Despite this large and growing population of cancer survivors,
the majority of studies of people with cancer, including breast
cancer, focus exclusively on the initial adjustment period. Far
fewer studies have examined predictors of long-term adjustment or
have examined the experience of surviving cancer. In recent years,
several methodologically strong studies of cancer survivorship
have emerged that examine the long-term effects (1–9 years) of
having cancer on quality of life (Bower et al., 2000; Dorval,
Maunsell, Deschenes, Brisson, & Masse, 1998; Ganz et al., 1996;
Ganz, Rowland, Desmond, Meyerowitz, & Wyatt, 1998; Meyerowitz, Desmond, Rowland, Wyatt, & Ganz, 1999). Overall, these
studies have concluded that there are no major differences in
general quality of life between cancer survivors and healthy controls. With one exception, these studies have examined a single
period of time or only two periods of time. One longitudinal study
examined adjustment to breast cancer over a 3–5-year period and
identified different stages of the illness that were associated with
Vicki S. Helgeson and Pamela Snyder, Department of Psychology,
Carnegie Mellon University; Howard Seltman, Department of Statistics,
Carnegie Mellon University.
We are grateful to Bobby Jones for his assistance with using and
understanding the SAS trajectory program.
Correspondence concerning this article should be addressed to Vicki S.
Helgeson, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213. E-mail: [email protected]
Social Resources
Social resources include the different kinds of support received
from family and friends. According to the stress-buffering hypothesis, social support reduces one’s vulnerability to psychological
3
HELGESON, SNYDER, AND SELTMAN
4
distress during times of stress (Cohen & Wills, 1985). Numerous
studies have examined the impact of social relationships during
times of stress, in particular the threat from breast cancer (Alferi,
Carver, Antoni, Weiss, & Durán, 2001; for a review see Blanchard,
Albrecht, Ruckdeschel, Grant, & Hemmick, 1995; Heim et al.,
1997; for reviews, see Helgeson & Cohen, 1996; Irvine, Brown,
Crooks, Roberts, & Browne, 1991; Molassiotis, Van Den Akker, &
Boughton, 1997; Rowland, 1989). These studies generally conclude that social support from family and friends is associated with
better adjustment to disease. The majority of these studies are
cross-sectional, however.
Some researchers have recognized that relationships are not
only sources of support but also can be sources of distress for
people with cancer (Dakof & Taylor, 1990; Peters-Golden, 1982;
Wortman & Dunkel-Schetter, 1987). Thus, research has examined
the impact of negative interactions on adjustment to cancer and
found that the negative aspects of close relationships show stronger links to adjustment than the positive aspects of close relationships (Butler, Koopman, Classen, & Spiegel, 1999; Manne, Taylor, Dougherty, & Kemeny, 1997).
Personal Resources
Personal resources include attributes of the person that one
brings to bear on a stressor such as cancer. One personal resource
is perceptions of personal control. In cross-sectional studies of
people with cancer, personal control has been associated with less
psychological distress and fewer role limitations (Bremer, Moore,
Bourbon, Hess, & Bremer, 1997; Ell, Mantell, Hamovitch, &
Nishimoto, 1989; Taylor, Lichtman, & Wood, 1984). A related
concept, self-efficacy, has been shown to be a positive correlate of
good adjustment among men with cancer (Beckham, Burker,
Lytle, Feldman, & Costakis, 1997).
Another personal resource is a sense of understanding of the
experience. The reverse, illness uncertainty or confusion about
what is happening with respect to the illness and the treatment, has
been associated with greater anxiety following hospital discharge
for mastectomy (Wong & Bramwell, 1992). Illness uncertainty
also has been associated with poor adjustment to gynecologic
cancer (Mishel, Hostetter, King, & Graham, 1984).
Optimism is a personal resource that has been linked to lower
levels of distress prior to breast cancer biopsy (Stanton & Snider,
1993), to positive adjustment to bone marrow transplant (Curbow,
Somerfield, Baker, Wingard, & Legro, 1993), and to generally
good adjustment among people with cancer (Mishel et al., 1984).
In a longitudinal study, optimism predicted positive adjustment to
breast cancer (Carver et al., 1993). Self-esteem also has been
associated with positive adjustment to breast cancer (for a review,
see Irvine et al., 1991).
Other Predictors of Adjustment
Aside from personal and social resources, what other characteristics of the person and the stressful event might distinguish
trajectories of functioning? Again, because this specific question
with regard to patterns of change has not been addressed by
previous research, we examine the literature on predictors of
adjustment to breast cancer (for a review, see Glanz & Lerman,
1992). Most of these studies focus on the initial adjustment period.
Among the demographic variables, education and income seem
to be unrelated to distress (Alferi et al., 2001; Maunsell, Brisson,
& Deschenes, 1992). Age is inversely related to psychological
distress among healthy populations (Blazer, 1989), but the relations of age to adjustment to breast cancer are contradictory (Glanz
& Lerman, 1992). One study showed no relation of age to distress
up to 18 months following breast cancer (Maunsell et al., 1992),
but several recent studies have shown that older people adjust
better to cancer (Williamson & Schulz, 1995), including breast
cancer (Stanton & Snider, 1993; Williamson, 2000). One reason
that older people may be less distressed is that they are more likely
than younger people to expect declines in their health.
There are aspects of the disease that may have implications for
adjustment to cancer. More severe illnesses are associated with
reduced physical functioning and increased psychological distress
(for a review, see Glanz & Lerman, 1992; Hagedoorn et al., 2000;
Williamson, 2000; Williamson & Schulz, 1992). Among women
with breast cancer, the nature of the surgery has been examined as
a predictor of adjustment. In general, there seem to be no differences in levels of distress or disease adjustment between women
who have lumpectomies or mastectomies (for a review, see
Kiebert, de Haes, & van de Velde, 1991). The only dimensions on
which women with lumpectomies fare better compared with
women with mastectomies are body image and sexual functioning
(Ganz, Schag, Lee, Polinsky, & Tan, 1992; for a review, see
Kiebert et al., 1991).
Another factor that may have implications for adjustment to
breast cancer is finding meaning in the experience, which has also
been referred to as posttraumatic growth and benefit finding. It is
not clear if this can be construed as a personal resource. Meaning
has been associated with adjustment to breast cancer, but the
direction of the relations are contradictory. In two cross-sectional
studies, posttraumatic growth was unrelated to distress among
breast cancer survivors (Cordova, Cunningham, Carlson, & Andrykowski, 2001) and bone marrow transplant survivors (Fromm,
Andrykowski, & Hunt, 1996). In another cross-sectional study,
positive growth was unrelated to mood but was related to greater
life satisfaction (Curbow et al., 1993). In one longitudinal study,
benefit finding was associated with increases in psychological
distress over time, especially for women with more advanced
disease (Tomich & Helgeson, 2002).
Finally, there is a large and growing literature on the effects of
psychosocial interventions on adjustment to cancer (for reviews,
see Helgeson & Cohen, 1996, and Meyer & Mark, 1995). The
literature shows that interventions clearly influence quality of life,
although all interventions are not equally effective. Some of the
women in the present study participated in a psychosocial group
intervention. One of the intervention components, education, focused on providing women with information and enhancing their
control over the illness experience. The education intervention had
positive effects on mental and physical functioning immediately
after it ended and 6 months later (Helgeson, Cohen, Schulz, &
Yasko, 1999). The peer discussion component focused on the
sharing of experiences and illness discussion with the goal of
providing emotional support and enhancing self-esteem. It had no
benefits and hints of some adverse effects on functioning shortly
after it ended. Some of the benefits of the educational intervention
remained 3 years later, and others disappeared with time (Helgeson, Cohen, Schulz, & Yasko, 2001). There were no long-term
ADJUSTMENT TO BREAST CANCER
effects, positive or negative, of the peer discussion intervention.
Again, none of these findings address the issue of whether the
interventions influenced trajectories of functioning. Thus, we examined whether the education and peer discussion interventions
distinguished different patterns of psychological and physical
functioning.
The Present Study
The goal of the research was twofold. First, we sought to
determine if we could identify distinct trajectories or patterns of
mental and physical functioning over 4 years following breast
cancer. We expected to identify at least one group of women
whose initial response to the illness would be impaired functioning
but who would rebound to better psychological and physical
functioning with time. We expected that the majority of women
would show an upward trend toward improved functioning with
time. We also expected to identify one group of women who would
show sustained low psychological and physical functioning over
the course of the study. To the extent that distinct trajectories could
be identified, our second goal was to examine whether we could
distinguish the different patterns of adjustment based on demographic variables, disease-related variables, and psychosocial variables. We expected that personal resources, social resources, and
stage of disease would distinguish among the trajectories. Because
the effect of the education intervention had dissipated with time
(Helgeson et al., 2001), we were uncertain as to whether it would
distinguish the trajectories.
5
interviewed in their homes for about 90 min. All instruments were administered orally, with the aid of response cards (e.g., 1 ⫽ never; 5 ⫽ always).
Quality of life was measured before the intervention, on average 4
months postdiagnosis (Time 1). By this time, 20% of the women had
recently completed chemotherapy. Quality of life was measured 7 months
postdiagnosis (Time 2; 1–2 weeks after the intervention), at which time
80% of the women had completed chemotherapy. We continued to measure
quality of life 13 months postdiagnosis (Time 3), 19 months postdiagnosis
(Time 4), 31 months postdiagnosis (Time 5), 43 months postdiagnosis
(Time 6), and 55 months postdiagnosis (Time 7). The only women included in the present analyses are those who were alive and had remained
disease free (i.e., no recurrence) throughout the duration of follow-up. We
omitted women who had sustained recurrences from the analyses (n ⫽ 76)
because our goal was to identify the different trajectories of adjustment to
the initial diagnosis of and treatment for breast cancer. Undoubtedly, a
recurrence of breast cancer would disrupt quality of life and have significant influences on adjustment trajectories depending on the time of the
recurrence.
Thus, the present analyses are based on 287 women who were alive and
disease free by the final follow-up (Time 7), which was about 4.5 years
since diagnosis. The number of women who completed each follow-up
assessment is as follows: Time 1, 287; Time 2, 279; Time 3, 266; Time 4,
265; Time 5, 269; Time 6, 273; and Time 7, 271. Although not all women
completed all waves of assessment, 94% of the sample completed the last
assessment (Time 7).
In the randomized trial, women were randomly assigned to one of four
conditions: peer group discussion group, education group, education plus
peer group discussion, or control group. There were seven separate groups,
each consisting of 8 –12 women, in all four conditions. The intervention
groups met once a week for 8 weeks.
Quality of Life
Method
Participants
Participants were 363 women diagnosed with Stage 1 (n ⫽ 91), Stage 2
(n ⫽ 250), and Stage 3 (n ⫽ 22) breast cancer. All women were treated
with surgery followed by adjuvant chemotherapy. Over two thirds (68%) of
women had lumpectomies rather than mastectomies (32%), which is consistent with the norms for the Pittsburgh area. The majority of women were
married (69%). Ages ranged from 27 to 75, with a mean of 48.3 (SD ⫽
9.79). Participants were Caucasian (93%), African American (7%), and
Hispanic (1%). Education was 4% less than high school, 33% high school
graduate, 26% some college education, 23% college graduate, and 14%
postgraduate training.
Procedure
Women were recruited for the study from the offices of more than 40
medical oncologists shortly after they began chemotherapy. In other words,
once a woman had surgery and made an appointment with a medical
oncologist for chemotherapy, she was eligible for the study. During one of
the first appointments with a medical oncologist, nurses provided the
names and phone numbers of patients who were interested in hearing about
the present study. Of 445 women contacted by phone, 364 (82%) agreed to
participate in the study, but only 312 (70%) agreed to be randomized to a
support group intervention (see Helgeson et al., 1999). That is, a subset of
the participants who refused randomization agreed to complete the assessment portion of the study and are included in these analyses (n ⫽ 52).
Briefly, there were no differences in quality of life between respondents
who did and did not agree to randomization, but those who declined
randomization were less interested in health issues and less educated
(Helgeson et al., 1999). The women who agreed to participate were
Health-related quality of life was measured at all waves with the SF-36
(Ware, Snow, Kosinski, & Gandek, 1993). This instrument has excellent
reliability and validity and has been used successfully to evaluate functional status in more than 20,000 depressed, chronically ill, and healthy
patients (Wells et al., 1989). It contains eight multi-item scales: General
Health Perception, Physical Functioning, Role Limitations Due to Physical
Problems, Bodily Pain, Social Functioning, Role Limitations Due to Emotional Problems, Energy/Vitality, and General Mental Health. In this study,
principal components analysis, followed by varimax rotation of the 36
items revealed eight factors with eigenvalues greater than 1. Items loaded
on their respective eight scales. The authors have derived factor weights for
the eight scales, so that a Mental Health Component Score (MCS) and a
Physical Health Component Score (PCS) can be created (Ware, Kosinski,
& Keller, 1994). Weights are assigned to all eight scales to create MCS and
PCS. The scales with the highest loading on PCS (in order from highest to
lowest) are Physical Functioning, Role Limitations Due to Physical Problems, Bodily Pain, and General Health Perceptions. The variables with the
highest loading on MCS are Mental Health, Role Limitations Due to
Emotional Problems, Social Functioning, and Energy/Vitality. Cronbach’s
alphas were high for each of the eight scales (above .80) at all times of
measurement. Higher numbers indicate better functioning. Thus, our primary outcomes are the MCS and PCS indices.
Chemotherapy Treatment Receipt
Treatment with chemotherapy was an eligibility requirement for the
study. We measured the extent to which women received the treatment
initially prescribed by their physicians. Dosages of treatment were often
reduced or treatment cycles delayed because of drug toxicity. The extent to
which a woman is able to receive the full dosage of treatment prescribed on
time predicts recurrence and survival (Bonadonna & Valagussa, 1981). For
6
HELGESON, SNYDER, AND SELTMAN
each drug prescribed, we calculated the amount of the drug received and
divided this by the amount of the drug prescribed. We then took the mean
of the percentages to be our index of chemotherapy treatment receipt.
Psychological Predictors of Trajectories of Functioning
All of the following instruments were administered during the baseline
interview.
Social resources. First, we developed a 15-item support scale based on
Vaux, Riedel, and Stewart’s (1987) measure of perceived availability of
support. Patients were provided with a list of ways that people help each
other and asked whether their friends and family would be able to provide
each kind of help. Responses were made on a 4-point scale: 1 ⫽ would not,
2 ⫽ might, 3 ⫽ probably, and 4 ⫽ certainly. Five items assessed emotional
support (e.g., “How likely would this person be to comfort you if you were
upset?”), five items assessed informational support (e.g., “How likely
would this person be to give you advice about what to do?”), and five items
assessed instrumental support (e.g., “How likely would this person be to
run an errand for you?”). During the initial interview, we administered all
three subscales with respect to the woman’s friends and family and also her
husband or partner. Three fourths (75%) of the women had partners.
Because the three kinds of support were highly correlated with one another,
we averaged across the items to compute partner support and family and
friend support indices. The internal consistencies of the two support scales
were good (␣ ⫽ .87 and .70, respectively).
Second, we developed a 10-item measure of negative interactions with
network members, based on research on failed support attempts (Dakof &
Taylor, 1990; Wortman & Lehman, 1985). Patients were told that people
may have good intentions but sometimes say or do something that upsets
them. Items included not understanding the situation, acting uncomfortable
when talking about the illness, changing the subject when trying to discuss
the illness, and trivializing problems. Women indicated the frequency with
which their family and friends engaged in each item on a 5-point scale (1 ⫽
never; 5 ⫽ very often). Women with partners also responded to this scale
with respect to their husbands or partners. The internal consistency of the
two scales was .81 for partner and .85 for family and friends. Many of these
items can be construed as the failure to provide emotional support (Helgeson & Cohen, 1996).
The support scales were highly related to the negative interactions scales
(r ⫽ ⫺.75, p ⬍ .001, for partner; r ⫽ ⫺.47, p ⬍ .001, for family and
friends). To create a more stable variable and reduce capitalizing on
chance, we created a social resources index by taking the mean of four
variables: support from family and friends and partner and reverse-scored
negative interactions for family and friends and partner. The average of two
variables was used for women without partners.
Personal resources. We measured four personal resources: selfesteem, body image, personal control, and illness uncertainty (reverse
weighted). We used the Rosenberg Self-Esteem Scale (Rosenberg, 1965) to
measure global self-esteem and developed a 14-item Body Image Scale
based on the Cancer Rehabilitation Evaluation System (Schag & Heinrich,
1988). Internal consistencies were high (␣ ⫽ .83 and .89, respectively). We
measured perceived personal control over the illness experience with three
items that have been used in previous studies with chronically ill populations (Campbell, Dunkel-Schetter, & Peplau, 1991; Collins, Taylor, &
Skokan, 1990; Helgeson, 1992). The three items tapped the domains of
future course of illness, day-to-day illness symptoms, and emotions related
to illness (␣ ⫽ .70). We administered an abbreviated form of the Illness
Ambiguity subscale from Mishel’s (1981) Uncertainty About Illness Scale
(␣ ⫽ .71). The nine items chosen had the highest loading on Mishel’s
factor analysis. Because all four of these scales loaded on a single principal
component following factor analysis and only one factor could be extracted, we developed a composite index that represented personal resources. Because the response scales for the four indices differed, we
standardized the four scores before summing them to create the index.
Benefit finding. We identified a number of ways that cancer could have
had a positive impact on people’s lives. Positive growth domains that were
represented included personal priorities (e.g., more grateful for each day),
daily activities (e.g., interest in participating in different activities), family
(e.g., more sensitive to family issues), world views (e.g., more aware and
concerned for the future of humankind), relationships (e.g., brought my
family closer together), career (e.g., inspired me to improve my job skills),
and religion (e.g., confirmed my faith in God). These items were adapted
from Behr, Murphy, and Summers’s (1991) Positive Contributions Scale
used with parents of disabled children. Participants rated the extent to
which 21 attitudes and behaviors had changed as a result of their stressful
experience (1 ⫽ not at all; 5 ⫽ very much). The internal consistency of the
scale was .95.
To make sure that benefit finding was distinct from the four personal
resources, we factor analyzed the four personal resources along with
benefit finding to see if a common factor could be extracted. The four
personal resources loaded highly on the first principal component (.51 to
.86); benefit finding loaded .20. Following varimax rotation, two factors
with eigenvalues greater than 1.0 emerged. The four personal resource
variables loaded on one factor, and benefit finding loaded on a separate
factor.
Overview of the Analysis
We used a new SAS procedure called TRAJ that Jones, Nagin, and
Roeder (2001) created to identify developmental trajectories of behavior.
Briefly, outcomes are treated as sensored normal data following a polynomial time course, given a discrete latent class assignment. TRAJ isolates
distinct trajectories (one for each latent class) and fits a mixture model to
calculate the probability of membership in each latent class for each
participant. The majority of people clearly falls in a single class. Effects of
covariates on class membership can then be modeled.
The first part of the analysis was aimed at identifying distinct trajectories
of mental and physical functioning. That is, we had to determine the
optimal number of trajectories that best represented distinct patterns of
change. We used the Bayesian Information Criterion (BIC) for determining
the number of groups. The BIC is a method analogous to the adjusted R2
in that it balances model complexity and model fit. Higher numbers
indicate a better fit. We also examined whether each trajectory was
characterized by a linear, quadratic, or cubic function. To determine the
best function, we examined the significance of all three parameters (linear,
quadratic, cubic) and dropped the ones that were nonsignificant. That is, we
started by including linear, quadratic, and cubic parameters for each
trajectory. If the cubic parameter was not significant, we dropped it and
tested for a quadratic trajectory. If this was not significant, we dropped it
and tested for a linear trajectory. We retained the linear parameter even if
this was not significant. After each change we compared the results using
the BIC criteria. Finally, we examined whether allowing the intercept and
slope of each trajectory to be a random effect improved the model fit. For
example, a random intercept would mean that all participants in a group
have a similar trajectory but can shift up and down on the y axis relative
to the group average. We began by allowing the intercept and slope to be
random parameters and examined their significance levels. Nonsignificant
random parameters were replaced with fixed parameters. Again, we compared models and selected the best model based on the BIC criteria.
Results
Model Selection: Identifying Distinct Trajectories
MCS. Table 1 shows that four distinct trajectories best represented the data for MCS. The BIC increased as the number of
trajectories increased up to four. In general, the more complex
model is justified if its BIC is at least three higher than the simpler
ADJUSTMENT TO BREAST CANCER
Table 1
Criteria for Selecting the Number of Trajectories
Score and
number of groups
MCS
1
2
3
4
5
PCS
1
2
3
4
5
6
7
BIC
Null
model
Change
in BIC
⫺6,703.76
⫺6,449.14
⫺6,373.38
⫺6,358.61
⫺6,376.70
0
1
2
3
4
254.62
75.76
14.77
⫺18.09
⫺6,521.95
⫺6,141.65
⫺6,063.42
⫺6,017.89
⫺6,018.92
⫺5,991.39
⫺5,995.82
0
1
2
3
4
5
6
380.30
78.23
45.53
⫺1.03
27.53
⫺4.43
Note. BIC ⫽ Bayesian Information Criterion; MCS ⫽ Mental Health
Component Score; PCS ⫽ Physical Health Component Score.
model. The change from four to five groups led to a decrease rather
than an increase in BIC. The four distinct trajectories are shown in
Figure 1. The solid lines represent the observed data; the broken
lines represent the predicted trajectory. Table 2 displays model
selection for the shape of the trajectories. For each of the four
trajectories, we started by including linear, quadratic, and cubic
parameters and then dropped the nonsignificant ones. The Model
1 (M1) columns show that the cubic parameter was only signifi-
7
cant for Trajectories (Traj) 1 and 2. Thus, in Model 2 (M2), the
cubic parameters were retained for Traj 1 and Traj 2 but dropped
from Traj 3 and Traj 4. Finally, in Model 3 (M3), we dropped the
quadratic parameter for Traj 3. Model 3 (M3) has the highest BIC
and is therefore the preferred model. Traj 1 and Traj 2 were cubic,
Traj 3 was linear, and Traj 4 was quadratic. The parameters in the
model form the equation for each trajectory. For example, the
equation for Traj 1 in Model 3 (M3) is: Traj 1 ⫽ 46.61 ⫹ (3.22 ⫻
time) ⫹ (⫺5.10 ⫻ time2) ⫹ (3.02 ⫻ time3). The top half of Table
3 shows which parameters were allowed to be random for each
trajectory. We began with a random effect for both the intercept
and slope for each of the four trajectories and dropped nonsignificant random parameters. The last column of Table 3, Model 6
(M6), shows that the intercept and the slope were allowed to be
random for Traj 1 and Traj 2; Traj 3 has a random intercept and a
fixed slope, and Traj 4 has a fixed slope and a fixed intercept.
As shown in Figure 1, Traj 4 reveals the highest levels of mental
functioning or the lowest levels of distress, and these levels are
fairly consistent throughout the duration of the study. There is
some improvement in functioning early on and then a slight
deterioration toward the end of the study, which is supported by
the quadratic function. This trajectory characterizes the largest
percentage of people (43%). Traj 3, characteristic of 18% of the
sample, shows minor ups and downs throughout the duration of the
study, but this variation was not captured by a cubic, quadratic, or
linear function. The levels of mental functioning for Traj 3 are
somewhat lower than Traj 4, showing a slight upward trend over
time. Traj 1 and Traj 2 reveal the most interesting patterns of
Figure 1. Four trajectories of mental functioning from 4 to 55 months (mos) after breast cancer diagnosis.
MCS ⫽ Mental Health Component Score; T ⫽ time postdiagnosis.
HELGESON, SNYDER, AND SELTMAN
8
Table 2
Model Selection for Mental Health Component Score
Model 1 (M1)
Traj group
and parameter
Traj 1
cubic
Traj 2
cubic
Traj 3
cubic
Model 2 (M2)
Traj 4
cubic
Traj 1
cubic
Traj 2
cubic
Traj 3
quad
Model 3 (M3)
Traj 4
quad
Traj 1
cubic
Traj 2
cubic
Traj 3
linear
Traj 4
quad
1
Intercept
Linear
Quad
Cubic
47.21
2.52*
⫺5.07***
3.34***
43.36
3.05**
⫺4.71***
3.02***
46.61
3.22**
⫺5.10***
3.02**
Intercept
Linear
Quad
Cubic
28.59
0.02
3.57***
⫺2.64**
28.35
0.05
3.58***
⫺2.66**
28.47
0.05
3.59***
⫺2.67**
Intercept
Linear
Quad
Cubic
44.27
0.65
0.22
⫺1.05
2
3
46.57
⫺0.38
⫺01.01
57.76
⫺1.02
4
Intercept
Linear
Quad
Cubic
119.21
2.94**
⫺2.79**
0.45
BIC ⫽ ⫺6,358.61
126.06
6.45***
⫺3.87***
127.23
6.46***
⫺4.04***
BIC ⫽ ⫺6,353.59
BIC ⫽ ⫺6,351.25
Note. Groups in bold were the models with the best fit. M ⫽ linear, quadratic, and cubic parameters of the Mental Health Component Score; Traj ⫽
trajectory; Quad ⫽ quadratic; BIC ⫽ Bayesian Information Criterion.
* p ⬍ .05. ** p ⬍ .01. *** p ⬍ .001.
change. Both revealed cubic functions. These two groups start out
with the lowest levels of mental functioning, but Traj 2 immediately and substantially improves over time with some deterioration
toward the end of the study, whereas Traj 1 shows an immediate
and substantial decline over time with a modest improvement
toward the end of the study. Traj 2 characterizes 27% of the
sample, and Traj 1 characterizes 12% of the sample. The crossover
in mental functioning between Traj 1 and Traj 2 occurs early on in
the study, within 6 months of diagnosis. By 13 months, the mental
functioning of Traj 2 and Traj 3 are indistinguishable.
PCS. Table 1 shows that four distinct trajectories best represent changes in PCS over time. The BIC increased up to four
groups and then decreased with the addition of a fifth group. There
was also an additional increase in BIC with six groups, however.
When we reviewed the six trajectories, we could not see that they
added useful information beyond that contained in a fourtrajectory model. In addition, the number of people captured by
each trajectory was becoming quite small with six trajectories (i.e.,
three of six trajectories contained less than 8% of the people).
Thus, for parsimony, we retained the four-trajectory model. Table
4 shows model selection for the shape of each of the four trajectories. Again, we began by testing the linear, quadratic, and cubic
parameters for all four trajectories. The cubic parameters in the
Model 1 (P1) columns were only significant for Traj 3 and Traj 4.
Thus, the cubic parameters were dropped from Traj 1 and Traj 2
and the quadratic parameters were tested in Model 2 (P2). The
final model, displayed in the Model 4 (P4) column, has the highest
BIC: Traj 1 and 2 are linear and Traj 3 and 4 are cubic. The bottom
half of Table 3 shows the test of random effects. Model 6 (P6) was
the best model: The intercept and the slope were allowed to be
random for Traj 1 and Traj 3; the intercept was fixed and the slope
was random for Traj 2; and the intercept was random and the slope
was fixed for Traj 4.
As shown in Figure 2, Traj 4 showed the best physical functioning at the start of the study, improved slightly with time, and
remained consistently high throughout the duration of the study.
This trajectory captures the majority of the people, 55%. The slight
changes that are captured by the cubic function are difficult to see.
By contrast, Traj 2 characterizes the smallest percentage of the
sample (2%), showed the worst physical functioning at the start of
the study, and slowly deteriorated over time. Traj 1 (20%) and Traj
3 (23%) both began with an intermediate level of physical functioning. Traj 1’s physical functioning does not appear to change
over time (linear function), whereas Traj 3 shows an immediate
improvement that is sustained throughout most of the study with
some deterioration toward the end (cubic function).
Identifying Variables That Distinguish Trajectories
MCS. Our goal was to distinguish Traj 1, the group who
deteriorated over time, from all other trajectories. We were especially interested in distinguishing Traj 1 from Traj 2, the group
who started at the same low level of mental functioning but
improved with time. First, we examined demographic and diseaserelated variables as risk factors for group assignment: age, education, stage of disease, number of positive lymph nodes, estrogenreceptor status, and surgery. The estimates that we report are the
change in log odds (lo) ratio of being in one trajectory versus
another, given a one-unit increase in the risk factor. For example,
if the coefficient for distinguishing Traj 2 from Traj 1 for no
ADJUSTMENT TO BREAST CANCER
Table 3
Random Effects Parameters by Trajectory (Traj) Group
MCS
Traj group
and parameter
Model 4 (M4)
Model 5 (M5)
Model 6 (M6)
Intercept
Slope
5.83***
4.62***
5.60***
5.62***
5.60***
4.63***
Intercept
Slope
6.77***
8.03***
6.77***
8.04***
6.79***
8.10***
Intercept
Slope
2.10*
0.01
2.20*
2.36*
1
2
3
4
Intercept
Slope
0.14
0.14
0.00
BIC ⫽ ⫺6,317.10 BIC ⫽ ⫺6,312.34 BIC ⫽ ⫺6,309.51
PCS
Model 5 (P5)
Model 6 (P6)
Intercept
Slope
4.72***
6.55***
4.73***
6.54***
Intercept
Slope
0.00
2.21*
0.02
2.21*
Intercept
Slope
3.70***
5.55***
3.73***
5.55***
1
2
3
4
Intercept
Slope
5.77***
5.80***
0.01
BIC ⫽ ⫺5,958.30 BIC ⫽ ⫺5,955.45
Note. M ⫽ linear, quadratic, and cubic parameters of Mental Health
Component Score (MCS); BIC ⫽ Bayesian Information Criterion; P ⫽
linear, quadratic, and cubic parameters of Physical Health Component
Score (PCS).
* p ⬍ .05. *** p ⬍ .001.
college (coded 0) versus college education (coded 1) is 1.0, then
the odds of being in Traj 2 versus Traj 1 is exp(1.0), or 2.7 as high
if a participant attended college. Among the demographic variables, age was marginally significant in distinguishing Traj 1 from
Traj 4 (lo ⫽ 0.05, p ⫽ .06), such that the women characterized by
Traj 1 were younger. Among the disease-related variables, type of
surgery distinguished Traj 1 from Traj 2 (lo ⫽ 1.36, p ⬍ .05), such
that women in Traj 1 were more likely to have a mastectomy than
were women in Traj 2. Chemotherapy receipt distinguished Traj 1
from Traj 2 (lo ⫽ 0.66, p ⬍ .05) and Traj 4 (lo ⫽ 1.06, p ⫽ .06),
such that Traj 1 had a lower level of receipt than the other two
groups. Next, we examined the extent to which the two interventions (education, peer discussion) distinguished the trajectories.
The peer discussion intervention distinguished Traj 1 from Traj 4
(lo ⫽ ⫺1.18, p ⬍ .05), such that people in Traj 1 were more likely
to have been assigned to a peer discussion intervention. The
education intervention did not distinguish the trajectories.
Finally, we examined our psychosocial variables: personal resources, social resources, and benefit finding. Personal resources
distinguished Traj 1 from Traj 3 (lo ⫽ 1.86, p ⬍ .005) and Traj 4
9
(lo ⫽ 3.93, p ⬍ .001). Traj 1 tended to have fewer personal
resources than Traj 3 and Traj 4. Social resources distinguished
Traj 1 from Traj 4 (lo ⫽ 2.90, p ⬍ .001). Traj 1 tended to have
fewer social resources than Traj 4. Benefit finding also distinguished Traj 1 from Traj 4 (lo ⫽ ⫺0.66, p ⬍ .05), such that Traj
1 reported greater benefit finding.
Thus, we entered the significant risk factors into the final model
to see which ones emerged as independent risk factors: age, type
of surgery, peer discussion intervention, compliance with chemotherapy, personal resources, social resources, and benefit finding.1
The only variable that distinguished Traj 1 from Traj 2 was type of
surgery, and it was marginal (lo ⫽ 1.29, p ⫽ .07). The peer
discussion intervention distinguished Traj 1 from Traj 3 (lo ⫽
⫺1.40, p ⬍ .05), whereas social resources was marginal (lo ⫽
0.99, p ⫽ .06). Traj 1 was distinguished from Traj 4 by both
personal resources (lo ⫽ 3.26, p ⬍ .001) and social resources (lo ⫽
1.74, p ⬍ .001).
PCS. Here, our goal was to distinguish Traj 1 from all other
trajectories, especially Traj 3, because although the two started out
with a similar level of functioning, Traj 3 improved but Traj 1 did
not. We decided not to focus on distinguishing Traj 2, the group
with the lowest levels of physical functioning, from all other
trajectories because it characterized only 2% of the sample. In the
course of focusing on Traj 1, we would distinguish it from Traj 2.
Among the demographic and disease-related variables, age distinguished Traj 1 from Traj 3 (lo ⫽ ⫺0.09, p ⬍ .001) and Traj 4
(lo ⫽ ⫺0.08, p ⬍ .005), such that older people were in Traj 1.
Number of positive lymph nodes distinguished Traj 1 from Traj 2
(lo ⫽ 0.44, p ⬍ .05) and Traj 3 (lo ⫽ 0.43, p ⬍ .01), such that Traj
1 had fewer positive lymph nodes. Chemotherapy receipt, benefit
finding, and the interventions did not distinguish the trajectories.
Personal resources, however, distinguished Traj 1 from Traj 2
(lo ⫽ ⫺1.68, p ⬍ .005) and Traj 4 (lo ⫽ 1.02, p ⬍ .001), such that
Traj 1 had more personal resources than Traj 2 but fewer personal
resources than Traj 4. Social resources distinguished Traj 1 from
Traj 2 (lo ⫽ ⫺1.12, p ⬍ .005), such that Traj 1 had higher social
resources than Traj 2.
In the final model, we entered the significant risk factors simultaneously: age, positive lymph nodes, personal resources, and
social resources.2 Age marginally distinguished Traj 1 from Traj 2
(lo ⫽ 0.10, p ⫽ .07) and significantly distinguished Traj 1 from
Traj 3 (lo ⫽ ⫺0.07, p ⬍ .05) and Traj 4 (lo ⫽ ⫺0.10, p ⬍ .001).
That is, Traj 1 was older than Traj 3 and Traj 4, but younger than
Traj 2. Number of positive lymph nodes marginally distinguished
Traj 1 from Traj 2 (lo ⫽ 0.34, p ⫽ .09) and significantly distinguished Traj 1 from Traj 3 (lo ⫽ 0.49, p ⬍ .01). Personal resources
significantly distinguished Traj 1 from Traj 2 (lo ⫽ ⫺2.67, p ⬍
.01), Traj 3 (lo ⫽ 0.81, p ⬍ .05) and Traj 4 (lo ⫽ 1.41, p ⬍ .001).
Social resources did not significantly distinguish the groups.
1
Although the education intervention did not significantly distinguish
the trajectories, we reran this final model with all of the risk factors and
included it. The education intervention was nonsignificant and did not alter
the results.
2
Although the education intervention and the peer discussion interventions did not distinguish the trajectories, we reran the final model with their
inclusion. Inclusion of the interventions did not alter the results.
Intercept
Linear
Quad
Cubic
Intercept
Linear
Quad
Cubic
Intercept
Linear
Quad
Cubic
Intercept
Linear
Quad
Cubic
Traj 1
cubic
Traj 3
cubic
137.23
1.09
⫺5.60***
3.56***
BIC ⫽ ⫺6,017.89
101.25
3.24**
⫺8.12***
3.19**
44.36
1.26
⫺0.04
⫺0.74
29.97
⫺2.33*
⫺1.52
1.08
Traj 2
cubic
Traj 4
cubic
Traj 1
quad
Traj 3
cubic
136.81
1.07
⫺5.57***
3.56***
BIC ⫽ ⫺6,013.01
99.74
3.31***
⫺8.09***
3.15**
48.07
1.21
⫺1.13
33.12
⫺3.18**
⫺1.02
Traj 2
quad
Model 2 (P2)
Traj 4
cubic
Traj 1
quad
Traj 3
cubic
137.41
1.10
⫺5.61***
3.57***
BIC ⫽ ⫺6,010.85
101.49
3.30***
⫺8.12***
3.14**
76.57
0.68
32.62
⫺3.16**
⫺1.25
Traj 2
linear
Model 3 (P3)
Traj 4
cubic
Traj 1
linear
Traj 3
cubic
137.20
1.09
⫺5.60***
3.56***
BIC ⫽ ⫺6,008.78
100.97
3.30***
⫺8.12***
3.14**
74.26
0.78
42.83
⫺4.72***
Traj 2
linear
Model 4 (P4)
Traj 4
cubic
Note. Groups in bold were the models with the best fit. P ⫽ linear, quadratic, and cubic parameters of Physical Health Component Score; Traj ⫽ trajectory; Quad ⫽ quadratic; BIC ⫽ Bayesian
Information Criterion.
* p ⬍ .05. ** p ⬍ .01. *** p ⬍ .001.
4
3
2
1
Traj group
and parameter
Model 1 (P1)
Table 4
Model Selection for Physical Health Component Score
10
HELGESON, SNYDER, AND SELTMAN
ADJUSTMENT TO BREAST CANCER
11
Figure 2. Four trajectories of physical functioning from 4 to 55 months (mos) after breast cancer diagnosis.
PCS ⫽ Physical Health Component Score; T ⫽ time postdiagnosis.
Overlap Between Trajectories
We examined the overlap in the trajectories for MCS and PCS.
We classified people into their most likely trajectory group and
conducted a chi-square analysis. The chi-square was significant,
␹2(9, N ⫽ 287) ⫽ 25.50, p ⬍ .01, suggesting that the MCS
groupings were not distributed equally across the PCS groupings
(or that PCS groupings were not distributed equally across MCS
groupings). The results are shown in Table 5, which displays the
number of people in each cell (MCS by PCS grouping); the
percentage of people in each cell is indicated in parentheses. The
greatest overlap occurred for PCS Traj 4 and MCS Traj 4, the
groups who started out with the highest level of functioning and
showed a slight improvement that was maintained over time. Of
the people classified in MCS Traj 4, 67% of those were classified
in PCS Traj 4. Of the people classified in PCS Traj 4, 53% of those
Table 5
Number (and Percentage) of People in MCS and PCS
Trajectories
MCS
trajectory
group
PCS trajectory group
1
2
3
4
1
2
3
4
8 (2.8)
19 (6.6)
15 (5.2)
14 (4.9)
3 (1.0)
1 (0.3)
2 (0.7)
—
9 (3.1)
21 (7.3)
10 (3.5)
27 (9.4)
15 (5.2)
35 (12.2)
25 (8.7)
83 (23.3)
Note. Dash represents no participants in this group. MCS ⫽ Mental
Health Component Score; PCS ⫽ Physical Health Component Score.
were classified in MCS Traj 4. There was much less overlap when
MCS and PCS Traj 3s were compared, which is not surprising
because the two trajectories are shaped differently. It is difficult to
compare the last two trajectories. We compared MCS Traj 2 (those
who began the study with poor psychological functioning that
quickly improved with time) with PCS Traj 1 (those who began the
study with intermediate physical functioning that did not change
over time) and found that 25% of the people in MCS Traj 2 were
classified in PCS Traj 1 and 34% of those classified in PCS Traj
1 were classified in MCS Traj 2. Finally, we compared MCS Traj
1 with PCS Traj 2—those characterized by the poorest functioning,
which deteriorated over time. Of those classified in MCS Traj 1,
only 9% were classified in PCS Traj 2. Of those classified in PCS
Traj 2, which was a very small percentage of the sample, 50%
were classified in MCS Traj 1.
There were noteworthy sections of nonoverlap. People categorized in any of the MCS trajectories were fairly equally represented in PCS Traj 4 (the highest level of physical functioning):
43% of those in MCS Traj 1, 46% of those in MCS Traj 2, 48% of
those in MCS Traj 3, and 67% of those in MCS Traj 4. The same
can be said of PCS Traj 3 (intermediate level of physical functioning): between 19% and 26% of each MCS trajectory were
found in PCS Traj 3.
We also examined whether baseline levels of MCS could distinguish the PCS trajectories and whether baseline levels of PCS
could distinguish the MCS trajectories. Baseline PCS only distinguished MCS Traj 1 from Traj 4. Similarly, baseline MCS only
distinguished PCS Traj 1 from Traj 4. Controlling for these baseline values did not alter the significance of the risk factors previously reported in the final equations.
HELGESON, SNYDER, AND SELTMAN
12
Thus, there was some overlap between the two trajectories, but
most of the overlap occurred in the highest functioning groups.
People who had the highest level of psychological functioning
over the course of the study were quite likely to be classified as
high in physical functioning over the course of the study. However, people who had a very high level of physical functioning
over the course of the study had a range of patterns of psychological functioning. The small number of people who had sustained
poor physical functioning were typically classified as low in psychological functioning. These findings suggest that good physical
functioning is a necessary but not sufficient prerequisite for good
psychological functioning.
Discussion
The results from the present study showed that there are indeed
distinct patterns of change in mental and physical functioning
following the diagnosis and treatment of breast cancer. Heim et al.
(1997) found that psychosocial adjustment to breast cancer remained fairly stable over a 3–5 year period, but they did not
examine their data to see whether there were distinct patterns of
change. There are distinct courses of adjustment to breast cancer.
In other words, time alone is not a sufficient predictor of adjustment to breast cancer, which is somewhat contrary to previous
research that has suggested time is the most critical factor in
predicting adjustment (Irvine et al., 1991; Meyerowitz, 1980).
Certainly, time was an important factor for some groups of women
but not all. The mental functioning of 43% of the women started
out very high and only showed a modest change throughout the 4
years of follow-up. Similarly, the physical functioning of 55% of
the women started out very high and showed very little change
throughout the 4 years of follow-up. Thus, for these groups of
women, time bore only a modest relation to their mental and
physical functioning.
There were other groups of women, however, who showed
declines or improvements in mental and physical functioning over
time. For mental functioning, a group representing just less than
20% of the sample showed a small but steady improvement over
time. Two other groups, however, showed extremely different
trajectories of adjustment. These two groups started out at equally
low levels of mental functioning, but the larger group (27%)
rapidly improved by 13 months and then remained steady over the
duration of the study, whereas the smaller group (12%) showed a
steady decline that plateaued at about 19 months. For physical
functioning, a very small percentage of women (2%) showed a low
level of physical functioning at the start of the study and steadily
deteriorated over the duration of follow-up. Another 20% of
women with a moderate level of physical functioning showed no
change over the duration of the study. Finally, a group of 23% of
women began with a moderate level of physical functioning,
improved dramatically by 13 months, and then remained high over
the duration of the study.
Was there a period of time during which most of the change
occurred? Averaging across women with breast cancer, Heim et al.
(1997) showed that the greatest change in adjustment to breast
cancer took place between hospitalization and the subsequent 3
months. We cannot directly test this hypothesis because our initial
interviews of women took place, on average, 4 months following
diagnosis. However, we did find that the greatest change took
place within the 4 –13-month window for the women who showed
substantial change. For mental functioning, there was not a dramatic change at any point in the follow-up for about 60% of the
sample. Among the other 40%, some showed a sharp improvement
and some showed a sharp deterioration within the first 13 months
of diagnosis. For physical functioning, the greatest change also
took place within the 13 months following diagnosis. Although
20% of the sample showed no change, slightly more than half
showed a steady improvement, 23% showed a more dramatic
improvement, and an extremely small percentage (2%) showed a
deterioration.
Although four distinct trajectories were identified for both mental and physical functioning, there was less overlap between the
trajectories than might be expected. The greatest overlap occurred
for the highest functioning groups. In addition, physical functioning was more strongly related to mental functioning than vice
versa. That is, two thirds of the highest physical functioning group
had the highest mental functioning, and half of the lowest physical
functioning group had the lowest mental functioning. However,
knowing a woman’s mental functioning trajectory did not provide
as much information about her physical functioning trajectory.
A second purpose of this study was to see whether we could
distinguish among these different patterns of adjustment to breast
cancer over time. Demographic variables and disease variables did
very little in terms of differentiating the trajectories, with the
exception of age. Age was an important predictor of the trajectories of physical functioning. The group with the lowest level of
physical functioning that deteriorated over time was the oldest.
The group with the highest physical functioning throughout the
study and the group with moderate physical functioning who
showed substantial improvement were the youngest groups. It is
surprising that stage of disease showed very little relation to the
patterns of functioning over time. This may be due to the fact that
we had a relatively homogenous group with respect to prognosis—
women who had largely Stage 1 and 2 disease who did not recur
over the duration of the study.
Consistent with stress and coping theory, the personal and social
resources that women had at the beginning of the study best
distinguished the different trajectories of mental and physical
functioning. When personal and social resources were considered
simultaneously, both distinguished trajectories of mental functioning, but only personal resources emerged as an independent predictor of physical functioning. Personal resources may have stronger effects than social resources because personal resources can
affect social resources (Hobfoll, 1989). Those with personal resources know when to call upon social resources and are more
likely to have social resources available.
The interventions played a relatively minor role in distinguishing trajectories of functioning. In previous work, Helgeson et al.
(1999, 2001) found benefits of the education intervention on
quality of life. These benefits appeared immediately after the
intervention; some disappeared with time and some were maintained over time. In the present study, we found no evidence that
the education intervention distinguished trajectories of functioning. Thus, the education intervention was not powerful enough to
predict the shape of women’s functioning over time. In that earlier
work Helgeson et al. (1999, 2001) found hints of early short-term
adverse effects of the peer discussion intervention but no significant effects either in the short term or the long term. In the present
ADJUSTMENT TO BREAST CANCER
study we found that the peer discussion intervention distinguished
between the lowest level mental functioning trajectory that declined with time and the highest mental functioning trajectory that
was maintained over time—in other words, the two most divergent
groups. People who were classified into the low and deteriorating
mental functioning trajectory were more likely to have been in the
peer discussion intervention. It may be that women who are highly
distressed do not fare well with peer group discussion. They may
require a more intensive individual-based intervention. The peer
discussion intervention may have exacerbated the distress of some
women who lacked personal and social resources. We do not want
to overinterpret this finding, however, because only one comparison showed a negative effect for peer discussion.
In some sense, we were disappointed that we could not further
distinguish some of the most interesting differences in trajectories.
For mental functioning, we did not make much progress in distinguishing between the two groups for whom, despite starting out at
the same low level of functioning, one improved and one deteriorated. In the end, the only variable that differentiated the two was
type of surgery such that the women who deteriorated were more
likely to have had a mastectomy. That effect was marginal in the
final analysis, however. For physical functioning, we noted that the
group who had a consistently moderate level of physical functioning had fewer personal resources than a group who started out at
the same moderate level and improved, but personal resources
were not unique in distinguishing these two groups. Basically, the
group with the lowest level of physical functioning had the least
personal resources, and the group with the highest level of physical
functioning had the most personal resources. What we had hoped
to explain was the difference between the two groups for whom,
despite starting out at the same level of physical functioning, one
improved and one did not.
Limitations
Before concluding, we must acknowledge several limitations of
this study. First, the majority of women in this study were middle
class and Caucasian. The extent to which these findings generalize
to women of other SESs and other racial and ethnic groups is
unknown. In fact, people of a lower economic class and minority
groups may face a host of other stressors that make coping with
breast cancer more difficult. Thus, we might expect a larger
percentage of people to be located in groups that show sustained
problematic functioning. Second, we are basing our claims on
people’s self-reports of their psychological and physical functioning. More clinically based assessments might reveal different
patterns of functioning.
Finally, there may be other factors that distinguish trajectories of
adjustment that we did not assess in our study. Some investigators
have found that a history of stressful life events predicts greater
distress in response to breast cancer (Butler et al., 1999; Maunsell
et al., 1992). We did not measure prior stressful life events in this
study. There also may be other stressful events that occurred over
the 4 years of follow-up that have affected women’s levels of
distress or physical functioning. Finally, perhaps there are other
psychological variables, such as coping styles, that could have
distinguished these women from one another.
13
Conclusion
In sum, the present study shows that the course of adjustment to
breast cancer is not the same for all women. There are distinct
patterns of change. Some women steadily improve with time,
whereas others show marked deteriorations or improvements in
functioning. Much of the change in mental and physical functioning occurs within the first 13 months of diagnosis. What can be
done to prevent the decline in mental functioning that characterizes
12% of women or the failure to regain physical functioning that
characterizes 22% of women? Certainly, a first step is to identify
these women and other antecedents to their decline or failure to
rebound. Rather than identify factors that uniquely characterize
women, we identified factors that did not characterize them—the
absence of personal and social resources. Aside from the importance age plays in distinguishing trajectories of physical functioning, the personal and social resources that women bring with them
to the study—their self-image, sense of control, and supportive
relations—affect the course of their long-term psychological and
physical adjustment to breast cancer.
References
Alferi, S. M., Carver, C. S., Antoni, M. H., Weiss, S., & Durán, R. E.
(2001). An exploratory study of social support, distress, and life disruption among low-income Hispanic women under treatment for early stage
breast cancer. Health Psychology, 20, 41– 46.
American Cancer Society. (2002). Breast cancer facts and figures:
2001–2002. Retrieved January 25, 2002, from http://www.cancer
.org/downloads/STT/BrCaFF2001.pdf
Beckham, J. C., Burker, E. J., Lytle, B. L., Feldman, M. E., & Costakis,
M. J. (1997). Self-efficacy and adjustment in cancer patients: A preliminary report. Behavioral Medicine, 23, 138 –142.
Behr, S. K., Murphy, D. L., & Summers, J. A. (1991). Kansas Inventory of
Parental Perceptions. Lawrence: University of Kansas.
Blanchard, C. G., Albrecht, T. L., Ruckdeschel, J. C., Grant, C. H., &
Hemmick, R. M. (1995). The role of social support in adaptation to
cancer and to survival. Journal of Psychosocial Oncology, 13, 75–95.
Blazer, D. G. (1989). The epidemiology of depression in late life. Journal
of Geriatric Psychiatry, 22, 35–52.
Bonadonna, G., & Valagussa, P. (1981). Dose-response effect of adjuvant
chemotherapy in breast cancer. New England Journal of Medicine, 304,
10 –15.
Bower, J. E., Ganz, P. A., Desmond, K. A., Rowland, J. H., Meyerowitz,
B. E., & Belin, T. R. (2000). Fatigue in breast cancer survivors: Occurrence, correlates, and impact on quality of life. Journal of Clinical
Oncology, 18, 743–753.
Bremer, B. A., Moore, C. T., Bourbon, B. M., Hess, D. R., & Bremer, K. L.
(1997). Perceptions of control, physical exercise and psychological
adjustment to breast cancer in South African women. Annals of Behavioral Medicine, 19, 51– 60.
Butler, L. D., Koopman, C., Classen, C., & Spiegel, D. (1999). Traumatic
stress, life events, and emotional support in women with metastatic
breast cancer: Cancer-related traumatic stress symptoms associated with
past and current stressors. Health Psychology, 18, 555–560.
Campbell, S. M., Dunkel-Schetter, C. A., & Peplau, L. A. (1991). Perceived control and adjustment to infertility among women undergoing in
vitro fertilization. In A. L. Stanton & C. A. Dunkel-Schetter (Eds.),
Infertility: Perspectives from stress and coping research (pp. 133–156).
New York: Plenum Press.
Carver, C. S., Pozo, C., Harris, S. D., Noriega, V., Scheier, M. F.,
Robinson, D. S., 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.
14
HELGESON, SNYDER, AND SELTMAN
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering
hypothesis. Psychological Bulletin, 98, 310 –357.
Collins, R. L., Taylor, S. E., & Skokan, L. A. (1990). A better world or a
shattered vision? Changes in life perspectives following victimization.
Social Cognition, 8, 263–285.
Cordova, M. J., Cunningham, L. L. C., Carlson, C. R., & Andrykowski,
M. A. (2001). Posttraumatic growth following breast cancer: A controlled comparison study. Health Psychology, 20, 176 –185.
Curbow, B., Somerfield, M. R., Baker, F., Wingard, J. R., & Legro, M. W.
(1993). Personal change, dispositional optimism, and psychological adjustment to bone marrow transplantation. Journal of Behavioral Medicine, 16, 423– 443.
Dakof, G. A., & Taylor, S. E. (1990). Victims’ perceptions of social
support: What is helpful from whom? Journal of Personality and Social
Psychology, 58, 80 – 89.
Dorval, M., Maunsell, E., Deschenes, L., Brisson, J., & Masse, B. (1998).
Long-term quality of life after breast cancer: Comparison of 8-year
survivors with population controls. Journal of Clinical Oncology, 16,
487– 494.
Ell, K. O., Mantell, J. E., Hamovitch, M. B., & Nishimoto, R. H. (1989).
Social support, sense of control, and coping among patients with breast,
lung or colorectal cancer. Journal of Psychosocial Oncology, 7, 63– 89.
Fromm, K., Andrykowski, M. A., & Hunt, J. (1996). Positive and negative
psychosocial sequelae of bone marrow transplantation: Implications for
quality of life assessment. Journal of Behavioral Medicine, 19, 221–240.
Ganz, P. A., Coscarelli, A., Fred, C., Kahn, B., Polinsky, M. L., &
Petersen, L. (1996). Breast cancer survivors: Psychosocial concerns and
quality of life. Breast Cancer Research and Treatment, 38, 183–199.
Ganz, P. A., Rowland, J. H., Desmond, K. A., Meyerowitz, B. E., & Wyatt,
G. E. (1998). Life after breast cancer: Understanding women’s healthrelated quality of life and sexual functioning. Journal of Clinical Oncology, 16, 501–514.
Ganz, P. A., Schag, A. C. C., Lee, J. J., Polinsky, M. L., & Tan, S. (1992).
Breast conservation versus mastectomy: Is there a difference in psychological adjustment or quality of life in the year after surgery? Cancer, 69,
1729 –1738.
Glanz, K., & Lerman, C. (1992). Psychosocial impact of breast cancer: A
critical review. Annals of Behavioral Medicine, 14, 204 –212.
Hagedoorn, M., Kuijer, R. G., Buunk, B. P., DeJong, G. M., Wobbes, T.,
& Sanderman, R. (2000). Marital satisfaction in patients with cancer:
Does support from intimate partners benefit those who need it most?
Health Psychology, 19, 274 –282.
Heim, E., Valach, L., & Schaffner, L. (1997). Coping and psychosocial
adaptation: Longitudinal effects over time and stages in breast cancer.
Psychosomatic Medicine, 59, 408 – 418.
Helgeson, V. S. (1992). Moderators of the relation between perceived
control and adjustment to chronic illness. Journal of Personality and
Social Psychology, 63, 656 – 666.
Helgeson, V. S., & Cohen, S. (1996). Social support and adjustment to
cancer: Reconciling descriptive, correlational, and intervention research.
Health Psychology, 15, 135–148.
Helgeson, V. S., Cohen, S., Schulz, R., & Yasko, J. (1999). Education and
peer discussion group interventions and adjustment to breast cancer.
Archives of General Psychiatry, 56, 340 –347.
Helgeson, V. S., Cohen, S., Schulz, R., & Yasko, J. (2001). Long-term
effects of educational and peer discussion group interventions on adjustment to breast cancer. Health Psychology, 20, 387–392.
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44, 513–524.
Irvine, N., Brown, B., Crooks, D., Roberts, J., & Browne, G. (1991).
Psychosocial adjustment in women with breast cancer. Cancer, 67,
1097–1117.
Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based
on mixture models for estimating developmental trajectories. Sociological Methods and Research, 29, 374 –393.
Kiebert, G. M., de Haes, J. C. J. M., & van de Velde, C. J. H. (1991). The
impact of breast-conserving treatment and mastectomy on the quality of
life of early-stage breast cancer patients: A review. Journal of Clinical
Oncology, 9, 1059 –1070.
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New
York: Springer.
Manne, S. L., Taylor, K. L., Dougherty, J., & Kemeny, N. (1997). Supportive and negative responses in the partner relationship: Their association with psychological adjustment among individuals with cancer.
Journal of Behavioral Medicine, 20, 101–125.
Maunsell, E., Brisson, J., & Deschenes, L. (1992). Psychological distress
after initial treatment of breast cancer: Assessment of potential risk
factors. Cancer, 70, 120 –125.
Meyer, T. J., & Mark, M. M. (1995). Effects of psychosocial interventions
with adult cancer patients: A meta-analysis of the randomized experiments. Health Psychology, 14, 101–108.
Meyerowitz, B. E. (1980). Psychosocial correlates of breast cancer and its
treatments. Psychological Bulletin, 87, 108 –131.
Meyerowitz, B. E., Desmond, K. A., Rowland, J. H., Wyatt, G. E., & Ganz,
P. A. (1999). Sexuality following breast cancer. Journal of Sex and
Marital Therapy, 25, 237–250.
Mishel, M. H. (1981). The measurement of uncertainty in illness. Nursing
Research, 30, 258 –263.
Mishel, M. H., Hostetter, T., King, B., & Graham, V. (1984). Predictors of
psychosocial adjustment in patients newly diagnosed with gynecological
cancer. Cancer Nursing, 7, 291–299.
Molassiotis, A., Van Den Akker, O. B. A., & Boughton, B. J. (1997).
Perceived social support, family environment and psychosocial recovery
in bone marrow transplant long-term survivors. Social Science and
Medicine, 44, 317–325.
Peters-Golden, H. (1982). Breast cancer: Varied perceptions of social
support in the illness experience. Social Science and Medicine, 16,
483– 491.
Rosenberg, M. (1965). Society and the adolescent self image. Princeton,
NJ: Princeton University Press.
Rowland, J. H. (1989). Interpersonal resources: Social support. In J. C.
Holland & J. H. Rowland (Eds.), Handbook of Psycho-oncology (pp.
58 –71). New York: Oxford University Press.
Schag, C. A. C., & Heinrich, R. L. (1988). CARES Cancer rehabilitation
evaluation system. Santa Monica, CA: Cares Consultants.
Stanton, A. L., & Snider, P. R. (1993). Coping with a breast cancer
diagnosis: A prospective study. Health Psychology, 12, 16 –23.
Taylor, S. E. (1983). Adjustment to threatening events: A theory of
cognitive adaptation. American Psychologist, 38, 1161–1173.
Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social
psychological perspective on mental health. Psychological Bulletin, 103,
193–210.
Taylor, S. E., Lichtman, R. R., & Wood, J. V. (1984). Attributions, beliefs
about control, and adjustment to breast cancer. Journal of Personality
and Social Psychology, 46, 489 –502.
Tomich, P. L., & Helgeson, V. S. (2002, April). A prospective study: The
effect of perceived “meaning in life” on quality of life for breast cancer
survivors. Paper presented at the annual meeting of the Society of
Behavioral Medicine, Washington, DC.
Vaux, A., Riedel, S., & Stewart, D. (1987). Modes of social support: The
social support behaviors (SS-B) Scale. American Journal of Community
Psychology, 15, 209 –237.
Ware, J. E., Kosinski, M., & Keller, S. D. (1994). SF-36 Physical and
Mental Health Summary Scales: A user’s manual. Boston: Health
Institute.
Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36
ADJUSTMENT TO BREAST CANCER
Health Survey: Manual and interpretation guide. Boston: Health
Institute.
Wells, K. B., Stewart, A., Hays, R. D., Burnam, M. A., Roger, W., Daniels,
M., et al. (1989). Detection of depressive disorder for patients receiving
prepaid or fee-for-service care: Results from the Medical Outcome
Study. Journal of the American Medical Association, 262, 3298 –3302.
Williamson, G. M. (2000). Extending the activity restriction model of
depressed affect: Evidence from a sample of breast cancer patients.
Health Psychology, 19, 339 –347.
Williamson, G. M., & Schulz, R. (1992). Physical illness and symptoms of
depression among elderly outpatients. Psychology and Aging, 7, 343–
351.
Williamson, G. M., & Schulz, R. (1995). Activity restriction mediates
15
the association between pain and depressed affect: A study of
younger and older adult cancer patients. Psychology and Aging, 10,
369 –378.
Wong, C. A., & Bramwell, L. (1992). Uncertainty and anxiety after
mastectomy for breast cancer. Cancer Nursing, 15, 363–371.
Wortman, C. B., & Dunkel-Schetter, C. A. (1987). Conceptual and methodological issues in the study of social support. In A. Baum & J. E.
Singer (Eds.), Handbook of psychology and health, Vol. 5: Stress (pp.
63–108). Hillsdale, NJ: Erlbaum.
Wortman, C. B., & Lehman, D. R. (1985). Reactions to victims of life
crises: Support attempts that fail. In I. G. Sarason & R. R. Sarason
(Eds.), Social support: Theory, research and applications (pp. 463–
489). Dordrecht, the Netherlands: Martinus Nijhoff.