Journal of Pediatric Psychology, Vol. 27, No. 2, 2002, pp. 155-165
© 2002 Society of Pediatric Psychology
Daily Hassles and Social Support as Predictors of Adjustment in Children With Pediatric Rheumatic Disease
1 University of Kansas Medical Center, 2 Children's Hospital and Health Center, San Diego, California, 3 Children's Mercy Hospital, Kansas City, Missouri, 4 University of Southern California School of Medicine, Los Angeles, California
All correspondence should be sent to Michael A. Rapoff, Dept. of Pediatrics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, Kansas 66160-7330.
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Abstract |
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Objective: To test hypotheses that social support moderates the effects of microstressors on the psychosocial adjustment of children with pediatric rheumatic diseases (PRDs) and that among multiple sources of support, classmate and parent support are significant predictors of adjustment, after controlling for demographic and disease severity variables.
Methods: Children with PRDs (N = 160 children; 8-17 years) were recruited from three pediatric rheumatology centers and completed measures of daily hassles, social support, depressive symptoms, and state and trait anxiety; their parents completed measures of internalizing and externalizing behaviors.
Results: Fewer daily hassles and higher social support predicted fewer adjustment problems. Among the sources of support, classmate and parent support were significant predictors. Tests for moderation were significant only for a Hassles x Classmate Support interaction in the prediction of depression. A plot of the interaction between hassles and classmate support showed that children with high classmate support had lower levels of depression than children with low classmate support under high or low levels of daily hassles. Furthermore, children with high classmate support had lower levels of depression under conditions of low versus high daily hassles.
Discussion: Results are consistent with a main effect rather than buffering model for social support.
Conclusions: Interventions should focus on management of daily hassles and increasing social support for children with PRDs.
Key words: hassles; social support; psychosocial adjustment; pediatric rheumatic disease.
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Introduction |
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Pediatric rheumatic diseases (PRDs) are chronic multisystem disorders, involving acute and chronic tissue inflammation of the musculoskeletal system, blood vessels, and skin (Cassidy & Petty, 1995
). Rheumatic diseases are more common in girls and
affect about 200,000 children in the United States. Juvenile rheumatoid
arthritis (JRA) is the most common PRD, accounting for 75% to 83% of rheumatic
diseases affecting children. The etiologies of PRDs are not known, but several
factors, such as infection, autoimmunity, trauma, stress, and genetic
predisposition are thought to be involved. PRDs are chronic disorders with
periodic exacerbation and remission of symptoms, such as joint swelling, pain,
and limitation of function. Because PRDs are not curable, the main goal of
treatment is disease management, which includes reducing pain, controlling
inflammation, maintaining function, and preventing deformities. Treatment
involves medication, physical therapy, patient and family education,
psychosocial support, and nutritional counseling
(Cassidy & Petty,
1995).
Consistent with studies on the psychosocial adjustment of children with
other chronic diseases (Lavigne &
Faier-Routman, 1992), children with PRDs are at an increased risk
for adjustment problems, particularly internalizing problems such as anxiety
and depression, compared to healthy or normative controls
(Billings, Moos, Miller, & Gottlieb,
1987; Daltroy et al.,
1992; Daniels, Moos, Billings,
& Miller, 1987; McAnarney,
Pless, Satterwhite, & Friedman, 1974;
Wallander, Varni, Babani, Banis, &
Wilcox, 1989). However, some studies have found no differences in
adjustment when comparing children with PRDs to healthy or normative controls
(Ungerer, Horgan, Chaitow, & Champion,
1988; Varni, Wilcox, &
Hanson, 1988; Zeltzer,
Kellerman, Ellenberg, Dash, & Rigler, 1980).
To delineate why some children with chronic disease, such as PRDs, adjust
better than others, research has begun to focus on testing multivariate
conceptual models to predict the observed variance in adjustment of children
with chronic disease. One model, proposed by Wallander and Varni
(1992), uses a stress and
coping framework to delineate risk and resistance factors as predictors of
adjustment. The model specifies three categories of risk factors: (1) disease
and disability parameters (e.g., severity of handicap); (2) functional
independence; and (3) psychosocial stressors (e.g., daily hassles), as well as
three categories of resistance factors: (1) intrapersonal (e.g.,
problem-solving ability); (2) social-ecological factors (e.g., social
support); and (3) stress processing (e.g., coping strategies). This study
focuses on one risk factor (daily hassles) and one resistance factor (social
support) as predictors of adjustment in children with PRDs, with demographics
and disease severity as control variables.
Previous studies with children who have rheumatic and other chronic
diseases have shown that higher stress increases risk for adjustment problems,
such as greater anxiety and depressive symptoms, while higher social support
reduces the risk for adjustment problems
(Varni, Katz, Colegrove, & Dolgin,
1994; Varni, Setoguchi,
Rappaport, & Talbot, 1991;
Wallander & Varni, 1989).
In this study, we were interested in testing stress and social support main
effect and interaction models in the prediction of adjustment in children with
PRDs. The main effect model suggests that social support is beneficial to a
person, irrespective of the level of stress, while the interaction or
"buffering" model posits that social support is beneficial only
under conditions of relatively high stress
(Cohen & Wills, 1985).
Empirical evidence supports both the main effect and buffering models of
social support in predicting the adjustment of children and adults, with or
without chronic disease. Social support seems to have a buffering effect when
assessed as perceived availability of support and has main effects when
assessed as social integration or the presence of a social network and
quantitative properties of that network, such as density or reciprocity
(Schreurs & de Ridder,
1997). We hypothesized that social support would act as a buffer
to stress because (1) we measured perceived availability of social support
that has supported buffering effects and (2) the buffering hypothesis may be
particularly relevant to chronically ill persons since a chronic disease could
be viewed as an accumulation of stressors
(Schreurs & de Ridder,
1997).
In addition to the main and buffering effects of social support, the
sources and types of support available to children may differentially predict
psychosocial adjustment. Children seek different types of support from various
people in their social networks (Belle,
1989). Parents often provide instrumental aid whereas friends
provide companionship (Furman &
Buhrmester, 1985). Previous studies of children with chronic
disease or disability suggest that different sources of support may
differentially affect adjustment. For example, classmate support (contrasted
with parent, friend, and teacher support) was found to be the strongest
predictor of adjustment in children with limb deficiencies
(Varni et al., 1991) and
cancer (Varni, Katz, et al.,
1994). However, another study found that family social support,
but not peer support, was a significant predictor of adjustment in children
with JRA (Varni et al., 1988).
Thus, further research is needed to examine the relative importance of
different sources and types of support on the adjustment of children with
chronic diseases. Given the sparse previous literature on sources of support,
we hypothesized that, among multiple sources of social support (classmate,
friend, parent, and teacher), classmate and parent support would be the best
predictors of adjustment.
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Method |
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Participants
Children with PRDs were recruited from three pediatric rheumatology centers: two in the midwest and one on the West Coast of the United States. Inclusion criteria were (1) 8 to 17 years of age, (2) English-speaking, and (3) diagnosed with a PRD for at least 3 months. Three hundred thirty-five children and their parents were approached over the course of the study. The most common reason for refusing participation was lack of time. A total of 222 families participated in the study. Among these families, 160 returned complete data. The sample consisted of 52 (32.5%) boys and 108 (67.5%) girls. The significantly larger percentage of girls in this sample is consistent with the prevalence of rheumatic disease in children. The mean age of the children was 12.11 years. Most participants were Caucasian (80.6%), 8.8% were African American, 6.3% were Spanish American, 1.9% were Asian American, and 2.5% reported as "other." The majority of participants (67.5%) had a diagnosis of JRA. Twenty children (12.5%) had spondyloarthropathy, 19 (3.6%) had systemic lupus erythematosus (SLE), and 34 (15.4%) were diagnosed with another PRD. The mean socioeconomic status (SES) score was 44.58, which corresponds to middle- to upper-middle class strata. One hundred twenty children (75%) were from two-parent households. On average, children had mild to moderate disease severity (M = 3.14, SD =.87, where 1 = remission, 5 = severe).
Procedure
The institutional review boards of the three pediatric rheumatology centers
approved this research protocol. Recruitment of participants was conducted
with the help of the rheumatology clinic staff, who identified children that
met the age (8-17) and diagnosis (3 months postdiagnosis) criteria. During
routine clinic visits, children who met entry criteria and their parents were
approached by a research assistant and were told that the purpose of the study
was to gather information about the experience of having a rheumatic disease.
Individuals who agreed to participate signed parental consent and child assent
forms and were given measures to complete at home and mail back to the
researchers. Parents and children were asked to complete measures
independently and not share their responses to the questionnaires. In this
study, 148 mothers (93%) completed the parent measures.
Predictor Variables
Demographics. Parents completed a demographics form that included
their child's age, gender, and ethnicity and family SES, calculated using the
Hollingshead (1975)
four-factor model.
Disease Severity. Following physical examinations and history
taking during routine clinic visits, the pediatric rheumatologists completed a
measure of disease severity, the Disease Activity Index (DAI;
Varni, Thompson, & Hanson,
1987). The DAI provides a global assessment of disease activity on
a five-point scale, with higher scores indicating greater disease severity.
The categories include 5 = severe, 4 = moderate, 3 = mild, 2 = quiescent (no
physical or laboratory signs, on medication), and 1 = remission (quiescent for
2 months without medication). The construct validity of the DAI has been
suggested by significant positive correlations with pain intensity and disease
activity (Varni et al.,
1987).
Daily Hassles. Patients completed the Children's Hassles Scale
(CHS), a 43-item questionnaire that assesses the occurrence and severity of
daily hassles (Varni et al.,
1996). Examples of the items include "You had trouble
learning something new," "Your father was mad at you for getting a
bad school report," and "Your mother and father were
fighting." The instructions do not specify a time frame and include the
following response options: "If it has happened to you, circle the
X"; "If it has happened to you, but it did not bother you, circle
the 0"; and "If it has happened to you, but it did bother you,
circle the number that goes with the face that shows how it made you
feel" (two choices: 1 = "sort of bad" or 2 = "very
bad"). Four scores can be computed from the CHS: (1) total number of
events that occurred; (2) number of neutral events that occurred (i.e., event
occurred but did not bother child); (3) number of negative events that
occurred; and (4) sum of the negative events' rating of bother (possible range
= 1 to 86, with higher scores indicating greater number and severity of
hassles). The sum of negative events' rating of bother was used in this study,
because previous studies using the CHS and its adult counterpart have
suggested that this score is correlated most highly with adjustment measures
(Compas, Slavin, Wagner, & Vannatta,
1986; Varni, Rubenfeld,
Talbot, & Setoguchi, 1989;
Zukerman, Oliver, Hollingsworth, &
Austrin, 1986). The CHS has demonstrated acceptable construct and
convergent validity and internal consistency reliability (Varni et al.,
1989,
1991; Zuckerman et al.,
1986.
Social Support. Patients completed a measure of perceived social
support, the Social Support Scale for Children (SSSC;
Harter, 1985). The SSSC was
designed to assess several sources of social support and positive regard that
the child may receive from significant others in his or her life. Four sources
of social support are measured by the SSSC: parents, teachers, classmates, and
friends. Each subscale has six items, for a total of 24 items, with higher
scores indicating greater perceived support. The SSSC has demonstrated
construct validity as it correlates positively with scores from the
Self-Perception Profile for Children, with correlations ranging from.28 to.49
(Harter, 1985). The internal
consistency reliabilities for the subscales range from.72 to.88
(Harter, 1985;
Varni & Katz, 1997).
Criterion Variables
Depressive Symptoms. Patients completed a measure of depressive
symptoms, the Children's Depression Inventory (CDI), a 27-item self-report
symptom-oriented scale used with children and adolescents with at least a
first-grade reading level (Kovacs,
1992). The CDI is the most widely used self-report measure of
depressive symptoms in children and has normative data from pediatric,
psychiatric, and school-based population samples. The CDI is designed to
measure the severity of the depressive symptoms, not to diagnose clinical
depression, with higher scores indicating greater severity of symptoms.
Coefficient alpha reliability across various childhood samples ranged from.71
to.87. The CDI has also shown acceptable construct and concurrent validity
(Kovacs, 1992).
State and Trait Anxiety. Patients completed anxiety measures, the
State and Trait Anxiety Inventory. Anxiety was measured by two versions of the
State and Trait Anxiety Inventory (STAI): the child form (STAIC;
Spielberger, 1973) and
adolescent form (STAIY; Spielberger,
1983). Because these two versions of the STAI are scored
differently, scores on each version were converted to standardized
(z) scores for statistical analyses. Both versions consist of 40
items that assess state (i.e., situational) and trait (i.e., characteristic)
components of anxiety. As might be expected, test-retest reliability is higher
for trait anxiety (.65 to.71) than for state (.31 to.47) anxiety. Internal
consistency reliability is high for both forms (.90 to.92). Both forms of the
STAI have demonstrated acceptable concurrent and construct validity
(Spielberger, 1973,
1983).
Behavior Problems. Parents completed an assessment of their
children's behavioral problems, the Child Behavior Checklist (CBCL;
Achenbach & Edelbrock,
1983). The CBCL consists of two parts: social competence (not
reported in this study) and behavior problems. The behavior component is
divided into two types of behaviors: internalizing (e.g., depression, anxiety)
and externalizing (e.g., oppositional, acting-out behaviors, and attention
problems) psychological symptoms. The CBCL is a standardized and empirically
based descriptive measure of children and adolescents' behavior problems that
has shown acceptable construct validity and content validity, as well as
internal consistency reliability
(Achenbach, 1991).
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Results |
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Data Analysis Procedures
Data analysis proceeded in three steps. In Step 1, we conducted one-way analysis of variance (ANOVA) to determine whether there were site differences on the predictor and criterion variables. In Step 2, we computed zero-order correlations among the predictor variables and between the predictor and criterion variables. In Step 3, we conducted hierarchical multiple regressions on each of the adjustment variables. For each of the regression analyses, only those predictors with a significant zero-order correlation with a particular criterion variable were entered into the regression model. If interactions were found, we conducted follow-up analyses to examine the degree and direction of the interaction.
Descriptive Analyses
Descriptive information for the measures is presented in
Table I. Before conducting
further analyses, we used ANOVA to test site differences on each of the
variables. No significant differences emerged between the sites for SES, daily
hassles, perceived social support, depression, state anxiety, trait anxiety,
internalizing behaviors, or externalizing behaviors. However, children
differed by site for age, F(2, 157) = 4.136, p =.018, and
disease severity, F(2, 157) = 3.295, p =.040. Post-hoc
analyses indicated that the children at the West Coast site were younger
(M = 11.20) than children at the second midwest site (M =
12.88) and that children at one midwest site had more severe disease
(M = 3.41) than the children at the other midwest site (M =
3.00). Because there were no other group differences and age and disease
severity were controlled for in the analyses, results from the three sites
were pooled.
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Scores on the daily hassles measures for our sample were similar to scores
reported in two studies on children with limb deficiencies
(Varni et al., 1989,
M = 23, SD = 15; Varni
et al., 1991, M = 21, SD = 14). The mean social
support scores for our sample were within the range of means found in
normative samples, with the exception that friend support was somewhat higher
for our sample (M = 3.49) versus normative samples (range of means =
2.87 to 3.42; see Harter,
1985). Scores on the CDI for our sample were clearly below the
level (> 12) considered clinically significant or at-risk for depression
(Kovacs, 1992). Similarly,
state and trait anxiety and externalization scores for our sample were within
the normal range. However, internalizing behaviors were slightly higher than
average.
Zero-Order Correlation Coefficients Among Predictor Variables
Zero-order correlation analyses were conducted to examine the
interrelationships among the predictor variables. Because familywise alpha
correction was not used, zero-order correlations at the.05 alpha level should
be interpreted cautiously.
As shown in Table II, older
age was associated with greater disease severity and lower teacher social
support. Gender (being female) was associated with higher classmate, friend,
and parent support. Higher daily hassles were associated with lower classmate
social. As might be expected, the four social support measures were
significantly correlated with each other. In spite of significant
intercorrelations among some of the predictor variables, none of these
correlations approached the level (.70 or higher) suggestive of significant
problems with multicollinearity
(Tabachnick & Fidell,
1996, p. 86).
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Zero-Order Correlation Coefficients Between Predictor and Criterion
Variables
Zero-order correlation analyses were conducted to test bivariate
associations between the predictor and criterion variables. Because familywise
alpha correction was not used, zero-order correlations at the.05 alpha level
should be interpreted cautiously.
As shown in Table III, older age was associated with higher levels of depression and state anxiety. Lower SES was associated with higher levels of externalizing and internalizing behavior problems. Greater daily hassles were associated with higher scores on all adjustment measures. Higher classmate social support was associated with lower scores on all adjustment measures. Higher friend social support was related to lower levels of depression and state anxiety. Greater parent social support was related to lower levels of depression, state anxiety, trait anxiety, and externalizing behavior problems. Greater teacher social support was related to lower scores on all adjustment measures.
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Hierarchical Multiple Regression Analyses
We conducted five hierarchical multiple regression analyses with child
adjustment measures (depression, state and trait anxiety, and externalizing
and internalizing behavior problems) as the criterion variables and child
hassles and social support as the predictor variables, with demographic and
disease severity as control variables. For each of the regression analyses,
only those predictors with a significant zero-order correlation with a
particular criterion variable were entered into the regression model. The
demographic/disease severity variables were entered simultaneously in the
first step, daily hassles was entered in the second step, social support
subscales were entered simultaneously in the third step, and the Daily Hassles
x Social Support interactions were entered in the fourth step. To
minimize problems of multicollinearity, daily hassles and social support
scores were "centered" (by subtracting each score from the sample
mean). Interaction terms were created by multiplying the centered hassles
scores by the centered social support scores
(Aiken & West, 1991;
Holmbeck, 1997). At each step
in the regressions, the increment in variance accounted for by the set of
predictors added at that step was tested for significance. Individual
predictors will be discussed only if the standardized beta weights were
significant when first entered into the regression equation and when all
predictors were included in the equation. Standardized beta weights for the
four social support subscales (classmate, friend, parent, and teacher) were
also examined to determine their relative predictive ability. Because we did
not use a correction factor, significant findings at p <.05 should
be interpreted with caution.
As shown in Table IV, each multiple regression predicting adjustment was significant. The regression model with all predictors explained between 18% of the variance in externalizing behaviors to 42% of the variance in depression. Overall, demographic and disease severity variables did not account for a large amount of variance in the criterion measures, ranging from 3% for internalizing behaviors to 5% for state anxiety and externalizing behaviors. However, individual predictors in this set were significant. Age was a significant predictor of depression and state anxiety; SES was a significant predictor of externalizing and internalizing behaviors.
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At step two of the regression equation, daily hassles added significant incremental variance in the prediction of all criterion variables, ranging from 5% for state anxiety to 25% for trait anxiety. At step three of the regression equation, social support also added significant incremental variance in the prediction of all criterion variables, ranging from 5% for trait anxiety to 17% in depression. Among the sources of social support, classmate support significantly predicted depression and internalizing behaviors. Parent support significantly predicted depression, state anxiety, and trait anxiety, and teacher support significantly predicted externalizing behaviors.
At step four in the regression equation, the only significant Hassles x Support interaction was in the prediction of depression. The interaction between daily hassles and social support added a significant 5% to the variance of depression (p <.05). Hassles x Classmate Support and Hassles x Teacher Support interactions were significant. To clarify the nature of these interactions, we ran two additional hierarchical multiple regression analyses by entering the demographic/disease severity variables, followed by daily hassles, the specific social support source of interest (classmate or teacher), and the relevant interaction between hassles and social support (classmate or teacher). These analyses failed to confirm a significant Hassles x Teacher Support interaction but did confirm the significant Hassles x Classmate Support interaction in the prediction of depression. A plot of the interaction between hassles and classmate support showed that children with high classmate support had lower levels of depression than children with low classmate support under high or low levels of daily hassles. Furthermore, children with high classmate support had lower levels of depression under conditions of low versus high daily hassles.
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Discussion |
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Our results failed to confirm the first hypothesis that social support moderates or buffers the relationship between daily hassles and adjustment problems. Instead, the results support a main effect model of support; that is, support is beneficial irrespective of the level of stress reported by children with PRDs. These results are consistent with a previous study (Varni et al., 1989
) that
found main effects for daily hassles and social support but no significant
interactions in predicting the adjustment of children with limb deficiencies.
Main effects of social support have also been found for adults and children
with various chronic diseases (Schreurs
& de Ridder, 1997). However, the buffering hypothesis need not
be abandoned in studies on psychosocial adjustment to chronic pediatric
diseases. Social support may act as a buffer under situations of acute stress
but not chronic, day-to-day stressful situations as measured in this study
(Alloway & Bebbing, 1987). Also, a daily hassles measure that taps
disease-related hassles, rather than generic hassles as assessed in this
study, might yield interactions with support as it would reflect the demands
of having a chronic disease (Schreurs
& de Ridder, 1997).
Our results did support the second hypothesis that among multiple sources
of support, classmate and parent support were the best predictors of
adjustment, relative to friend or teacher support. These results are
consistent with previous studies of children with cancer
(Varni et al., 1994), JRA
(Varni et al., 1988), limb
deficiencies (Varni et al.,
1989), and a mixed sample of children with chronic health problems
(Wallander & Varni,
1989).
The child development and social support literatures suggest that children
receive different types of support from different people in their social
environment (Belle, 1989;
Furman & Buhrmester,
1985). Types or functions of support delineated in the literature
include "esteem" or "emotional" support (actions that
convey the child's value or worth); "informational" support
(advice or guidance on coping with problems); "instrumental"
support (providing tangible resources or services), and
"companionship" support (sharing social activities with another
person). The measure of support used in this study (the SSSC) appears to tap
these different types of support provided by various people in the child's
social environment (Harter,
1985). The SSSC classmate support scale seems to tap esteem and
companionship dimensions of support (e.g., the extent to which the child is
"liked the way they are" and that classmates "ask them to
join in play or games") and the absence of teasing by classmates (e.g.,
"don't make fun of them"). The SSSC parent support scale appears
to primarily tap esteem support (e.g., the extent to which the parents
"care about their feelings," "treat them like a person who
really matters," and "like them the way they are"). Thus,
children with PRDs who perceive that they are successfully integrated into
their peer groups (and who are not teased to any significant degree) and
receive esteem or emotional support from their parents are likely to have
better emotional and behavioral adjustment.
DAily hassles also had main predictive effects in our study, accounting for
between 5% (for state anxiety) and 25% (for trait anxiety) of the variance in
the adjustment measures after controlling for demographic and disease severity
variables. This is consistent with one study that also found main effects for
daily hassles in predicting depression in children with limb deficiencies
(Varni et al., 1989). Daily
hassles have also predicted pain intensity in children with PRDs
(Varni et al., 1996). Despite
controversies concerning the extent to which daily hassles are confounded with
mental health outcomes, measuring daily hassles may be more useful than
assessing major life events (e.g., parents divorce) in predicting
psychological and somatic adaptational outcomes
(Holm & Holroyd,
1992).
Our study had several strengths including recruitment of patients at
multiple sites, multiple measures of adjustment utilizing both children and
parents as informants, and a larger sample size compared to previous studies.
Also, we explicitly tested for moderating effects of social support by
following recommended statistical strategies that have been underused in the
pediatric psychology literature (Holmbeck,
1997). However, consistent with similar studies on adjustment of
children with chronic diseases (Lavigne
& Faier-Routman, 1992;
Wallander & Varni, 1992),
our study had several limitations: (1) the cross-sectional and correlational
nature of the study limits conclusions about directionality of effects; (2)
our sample, although recruited from multiple sites, was one of convenience and
may not be representative of children and adolescents with PRDs, as we had a
high refusal rate and we did not collect demographic or disease-related data
on those who declined to participate; (3) the restricted range of scores on
the criterion measures (most being within the normative ranges) may have
attenuated relationships found between predictor and criterion variables in
the zero-order correlational and multiple regression analyses; (4) we had to
limit the number of predictor variables (risk and resource factors) from the
Wallander and Varni model that could be tested in this study to avoid
burdening participants and straining the participants-to-variable ratio in the
regression analyses; (5) we did not use a familywise error rate correction in
our five regression analyses (in order to maximize power and because the major
findings were significant at the more stringent alpha level of <.01); and
(6) although we stressed in our instructions to patients and their parents
that they complete measures independently and not share their responses to the
questionnaires, we cannot independently confirm that our instructions were
followed, as they completed the measures in their homes and mailed the
questionnaires back to us.
The results from this study suggest several clinical implications. Greater
perceived social support, particularly classmate and parental support, is
related to less emotional and behavioral adjustment problems for children with
PRDs. Social support interventions that target classmate and parental support
could protect children with PRDs from experiencing significant adjustment
problems. For example, Varni and his colleagues
(Varni, Katz, Colegrove, & Dolgin,
1993) developed a social skills training program for children with
newly diagnosed cancer that taught them problem-solving strategies,
assertiveness skills, and techniques for dealing with teasing and
name-calling. Children receiving this training reported higher perceived
classmate and teacher social support, and their parents reported less
internalizing and externalizing problems relative to children in a standard
social integration treatment group. Perceived stress as measured by daily
hassles was also an important predictor of adjustment in this study. This
suggests that standard stress management training, such as cognitive
restructuring and relaxation exercises, which have been effective in reducing
pain in children with PRDs, may also be effective in reducing adjustment
problems (Varni, Rapoff, & Waldron,
1994).
Future studies should increase the size and representativeness of samples
by conducting multisite investigations and should collect longitudinal data to
better address directional effects of putative causal relations. Attention
should also be given to developing disease-specific measures of daily hassles
and social support, as they may better predict adaptation to chronic disease,
adherence to medical regimens, and health outcomes compared to generic
measures (La Greca & Schuman,
1999). Also, studies should focus on delineating the types of
support children receive from different people in their social network.
Children with PRDs and other chronic diseases could benefit from interventions
that help them manage daily hassles and elicit support from significant others
in their social environment.
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Acknowledgments |
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This study was supported in part by a Clinical Research Grant from the national office of the Arthritis Foundation. Special thanks to Dr. Sam Green for his statistical consultations. We are also grateful to the children and their families who gave their time and energy to participate in this project without compensation.
Received July 31, 2000; revision received March 7, 2001; accepted May 11, 2001
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References |
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TopAbstractIntroductionMethodResultsDiscussionReferences |
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Achenbach, T. M. (1991). Manual for the Child Behavior Checklist/4-18 and 1991 profile. Burlington, VT: University of Vermont Department of Psychiatry.
Achenbach, T. M., & Edelbrock, C. (1983). Manual for the Child Behavior Checklist and revised Child Behavior Profile. Burlington, VT: University Associates in Psychiatry.
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.
Alloway, R., & Bebbington, P. (1987). The buffering theory of social support: A review of the literature. Psychological Medicine, 17, 91-108.[Web of Science][Medline]
Belle, D. (Ed.). (1989). Children's social networks and social supports. New York: Wiley.
Billings, A. G., Moos, R. H., Miller, J. J., Gottlieb, J. E. (1987). Psychosocial adaptation in juvenile rheumatic disease: A controlled evaluation. Health Psychology, 6, 343-359.[Web of Science][Medline]
Cassidy, J. T., & Petty, R. E. (1995). Textbook of pediatric rheumatology (3rd ed.). Philadelphia: W. B. Saunders.
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98, 310-357.[Web of Science][Medline]
Compas, B. E., Slavin, L. A., Wagner, B. A., & Vannatta, K. (1986). Relationship of life events and social support with psychological dysfunction among adolescents. Journal of Youth and Adolescence, 15, 205-221.
Daltroy, L. H., Larson, M. G., Eaton, H. M., Patridge, A. J.,
Pless, I. B., Rogers, M. P., & Liang, M. H. (1992).
Psychosocial adjustment in juvenile arthritis. Journal of Pediatric
Psychology, 17,
277-289.
Daniels, D., Moos, R. H., Billings, A. G., & Miller, J. J. (1987). Psychosocial risk and resistance factors among children with chronic illness, healthy siblings, and healthy controls. Journal of Abnormal Child Psychology, 15, 295-308.[Web of Science][Medline]
Furman, W., & Buhrmester, D. (1985). Children's perceptions of the personal relationships in their social networks. Developmental Psychology, 21, 1016-1024.[Web of Science]
Harter, S. (1985). Manual for the Social Support Scale for Children. Denver: University of Denver.
Hollingshead, A. B. (1975). Four factor index of social status. Unpublished manuscript. New Haven, CT: Yale University.
Holm, J. E., & Holroyd, K. A. (1992). The Daily Hassles Scale (revised): Does it measure stress or symptoms? Behavioral Assessment, 14, 465-482.
Holmbeck, G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 65, 599-610.[Web of Science][Medline]
Kovacs, M. (1992). Children's Depression Inventory manual. North Tonawanda, NY: Multi-Health Systems.
La Greca, A. M., & Schuman, W. B. (1999). Research methods in pediatric psychology. In P. C. Kendall, J. N. Butcher, & G. N. Holmbeck (Eds.), Handbook of research methods in clinical psychology (2nd ed., pp. 537-561). New York: Wiley.
Lavigne, J. V., & Faier-Routman, J. (1992).
Psychological adjustment to pediatric physical disorders: A meta-analytic
review. Journal of Pediatric Psychology,
17, 133-157.
McAnarney, E. R., Pless, B. P., Satterwhite, M. A., & Friedman,
S. B. (1974). Psychological problems of children with chronic
juvenile arthritis. Pediatrics,
53, 523-528.
Schreurs, K. M. G., & de Ridder, D. T. D. (1997). Integration of coping and social support perspectives: Implications for the study of adaptation to chronic diseases. Clinical Psychology Review, 17, 89-112.[Web of Science][Medline]
Spielberger, C. D. (1973). STAIC preliminary manual. Palo Alto, CA: Consulting Psychologists Press.
Spielberger, C. D. (1983). Manual for the State-Trait Anxiety Inventory (STAI) Form Y. Palo Alto, CA: Consulting Psychologists Press.
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). New York: HarperCollins.
Ungerer, J. A., Horgan, B., Chaitow, J., & Champion, G. D.
(1988). Psychosocial functioning in children and young adults
with juvenile arthritis. Pediatrics,
81, 195-202.
Varni, J. W., & Katz, E. R. (1997). Stress, social support, and negative affectivity in children with newly diagnosed cancer: A prospective transactional analysis. Psycho-Oncology, 6, 267-278.[Medline]
Varni, J. W., Katz, E. R., Colegrove, J. R., & Dolgin, M.
(1993). The impact of social skills training on the adjustment of
children with newly diagnosed cancer. Journal of Pediatric
Psychology, 18,
751-767.
Varni, J. W., Katz, E. R., Colegrove, J. R., & Dolgin, M. (1994). Perceived social support and adjustment of children with newly diagnosed cancer. Journal of Developmental and Behavioral Pediatrics, 15, 20-26.[Web of Science][Medline]
Varni, J. W., Rapoff, M. A., & Waldron, S. A. (1994). Juvenile rheumatoid arthritis: Psychological issues. In R. A. Olson, L. L. Mullins, J. B. Gillman, & J. M. Chaney (Eds.), The sourcebook of pediatric psychology (pp. 75-89). Boston: Allyn & Bacon.
Varni, J. W., Rapoff, M. A., Waldron, S. A., Gragg, R. A., Bernstein, B. H., & Lindsley, C. B. (1996). Effects of perceived stress on pediatric chronic pain. Journal of Behavioral Medicine, 19, 515-528.[Web of Science][Medline]
Varni, J. W., Rubenfeld, L. A., Talbot, D., & Setoguchi, Y. (1989). Determinants of self-esteem in children with congenital/acquired limb deficiencies. Journal of Developmental and Behavioral Pediatrics, 10, 13-16.[Web of Science][Medline]
Varni, J. W., Setoguchi, Y., Rappaport, L. T., & Talbot, D. (1991). Effects of stress, social support, and self-esteem on depression in children with limb deficiencies. Archives of Physical Medicine and Rehabilitation, 72, 1053-1058.[Web of Science][Medline]
Varni, J. W., Thompson, K. L., & Hanson, V. (1987). The Varni/Thompson Pediatric Pain Questionnaire: I. Chronic musculoskeletal pain in juvenile rheumatoid arthritis. Pain, 28, 27-38.[Web of Science][Medline]
Varni, J. W., Wilcox, K. T., & Hanson, V. (1988). Mediating effects of family social support on child psychological adjustment in juvenile rheumatoid arthritis. Health Psychology, 7, 421-431.[Web of Science][Medline]
Wallander, J. L., & Varni, J. W. (1989). Social support and adjustment in chronically ill and handicapped children. American Journal of Community Psychology, 17, 185-201.[Web of Science][Medline]
Wallander, J. L., & Varni, J. W. (1992). Adjustment in children with chronic physical disorders: Programmatic research on a disability-stress-coping model. In A. M. La Greca, L. J. Siegel, J. L. Wallander, & C. E. Walker (Eds), Stress and coping in child health (pp. 279-298). New York: Guilford.
Wallander, J. L., Varni, J. W., Babani, L., Banis, H. T., &
Wilcox, K. T. (1989). Family resources as resistance factors for
psychological maladjustment in chronically ill and handicapped children.
Journal of Pediatric Psychology,
14, 157-173.
Zeltzer, L., Kellerman, J., Ellenberg, L., Dash, J., & Rigler, D. (1980). Psychologic effects of illness in adolescence. II. Impact of illness in adolescents—Crucial issues and coping styles. Journal of Pediatrics, 97, 132-138.[Web of Science][Medline]
Zukerman, L. A., Oliver, J. M., Hollingsworth, H. H., & Austrin, H. (1986). A comparison of life events scoring methods as predictors of psychological symptomatology. Journal of Human Stress, 12, 64-70.[Web of Science][Medline]
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