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Journal of Pediatric Psychology logoLink to Journal of Pediatric Psychology
. 2013 Apr 12;38(7):722–731. doi: 10.1093/jpepsy/jst016

The Relationships Between Fatigue, Quality of Life, and Family Impact Among Children With Special Health Care Needs

I-Chan Huang 1,2,✉, Mary Anderson 2, Pranav Gandhi 3, Sanjeev Tuli 4, Kevin Krull 5, Jin-Shei Lai 6, John Nackashi 4, Elizabeth Shenkman 1,2
PMCID: PMC3721186  PMID: 23584707

Abstract

Objective To examine the relationships among pediatric fatigue, health-related quality of life (HRQOL), and family impact among children with special health care needs (CSHCNs), specifically whether HRQOL mediates the influence of fatigue on family impact. Methods 266 caregivers of CSHCNs were studied. The Pediatric Quality of Life Inventory Multidimensional Fatigue Scale, Pediatric Quality of Life Inventory Generic Scale, and Impact on Family Scale were used to measure fatigue, HRQOL, and family impact, respectively. Linear regressions were used to analyze the designated relationships; path analyses were performed to quantify the mediating effects of HRQOL on fatigue–family impact relationship. Results Although greater fatigue was associated with family impact (p < .05), the association was not significant after accounting for HRQOL. Path analyses indicated the direct effect of fatigue on family impact was not significant (p > .05), whereas physical and emotional functioning significantly mediated the fatigue–family impact relationship (p < .001). Conclusion Fatigue is related to family impact among CSHCNs, acting through the impairment in HRQOL.

Keywords: children with special health care needs, chronic condition, family impact, fatigue, quality of life

Introduction

As a result of improved treatments and health services, many children survive acute and life-threatening conditions and live with chronic conditions (CCs) (Dodge et al., 1997; Groothoff, 2005; Stiller, 2002). Recognizing that children with different CCs may share similarity in physical, psychosocial, economic, and rehabilitation outcomes, the Maternal and Child Health Bureau of the U.S. Department of Health and Human Services developed the concept of “children with special health care needs” (CSHCNs) (McPherson et al., 1998). Prevalence of CSHCNs ranges from 12% to 31% (van der Lee, Mokkink, Grootenhuis, Heymans, & Offringa, 2007).

Fatigue is one of the most prevalent symptoms that children may experience. The National Longitudinal Study of Adolescent Health (Add Heath) indicated that 21% of U.S. children aged 7–12 years (Rhee, Miles, Halpern, & Holditch-Davis, 2005) and 15–30% of adolescents aged 11–21 years reported frequent fatigue (Rhee, 2005). CSHCNs, including those with cancer (Varni, Burwinkle, Katz, Meeske, & Dickinson, 2002), diabetes (Varni, Limbers, Bryant, & Wilson, 2009), and rheumatology (Varni, Burwinkle, & Szer, 2004), had significant fatigue compared with healthy/reference groups.

For CSHCNs, fatigue is a critical contributor to impaired functional status (Edwards, Gibson, Richardson, Sepion, & Ream, 2003), school absenteeism (Garralda & Rangel, 2004), and poor health-related quality of life (HRQOL; Eddy & Cruz, 2007). Additionally, fatigue is the only factor significantly associated with impaired HRQOL of pediatric cancer survivors, outweighing the influence of other symptoms such as pain (Meeske, Patel, Palmer, Nelson, & Parow, 2007).

The types of fatigues may vary across CSHCNs, and there is a clear distinction in the definitions of different fatigue measures. The typical definitions include prolonged fatigue (fatigue lasting 1 month or longer), chronic fatigue (fatigue lasting 6 months or longer), and chronic fatigue syndrome (fatigue lasting 6 months or longer and accompanied by four of eight prespecified symptoms) (Rimes et al., 2007). In addition, fatigue is regarded as a multidimensional concept. Varni et al. (2002) categorized fatigue by three dimensions: general fatigue, sleep/rest fatigue, and cognitive fatigue, which are included in the Pediatric Quality of Life Inventory (PedsQL) Multidimensional Fatigue Scale (MFS). Although the use of different definitions for fatigue may have different clinical implications (Rimes et al., 2007), different measures share the common element of impact of fatigue on daily activities and HRQOL.

Caring for CSHCNs is a great undertaking for family members and can significantly affect the family’s daily functioning and routine activities (i.e., family impact). Symptoms [e.g., fatigue (Iobst, Nabors, Brunner, & Precht, 2007) and pain (Jastrowski Mano, Khan, Ladwig, & Weisman, 2011)] related to diseases and treatments are the leading cause of impact on family functioning. This is because parents assume the primary responsibility as caregivers to manage special needs associated with worrisome symptoms, complications, or emergencies. In particular, parent–child interactions, ties with relatives and friends, family cohesion, conflict, and problem-solving skills must be adjusted to disease management (Ray, 2002). Gibson et al. suggest that among children with cancer, 50% of them reporting cancer-related fatigue had a major effect on family lifestyle, such as ability to work and enjoy life, as well as the relations with family members and friends (Gibson, Garnett, Richardson, Edwards, & Sepion, 2005).

Although several pediatric studies have examined the relationships between fatigue and HRQOL (Eddy & Cruz, 2007), and between fatigue and family impact (Gibson et al., 2005; Iobst et al., 2007), the pathway underlying pediatric fatigue, pediatric HRQOL, and family impact remains unclear. Wilson and Cleary’s (1995) HRQOL model serves as a useful framework to guide the investigation into the relationships among fatigue, HRQOL, and family impact. This model articulates that demographic (e.g., age and gender), clinical (e.g., treatment), and psychosocial (e.g., personality and social support) factors contribute to an individual’s HRQOL; however, symptoms (e.g., fatigue) play a more central role than other variables in influencing HRQOL. Symptoms are viewed as the most proximal attributes to the disease process, thus can potentially exert direct effects on HRQOL. Individuals with poor HRQOL have associated impairments in broader aspects of well-being, including family impact and overall quality of life. In contrast, sociodemographics, diagnosis, and treatment do not have immediate or direct relevance to HRQOL and family impact.

This study aims to investigate the relationships among subjective perception of pediatric fatigue, pediatric HRQOL, and family impact in CSHCNs. We hypothesized that pediatric fatigue will have effects on pediatric HRQOL and family functioning; however, the effect of pediatric fatigue and family functioning will be partially explained and mediated by pediatric HRQOL. We conducted path analyses to quantify the direct and indirect effects of fatigue on family impact by accounting for HRQOL as a mediating factor. Assessing pediatric fatigue along with HRQOL provides a great insight into health status, which can help in designing appropriate interventions to improve family functioning for those caring for CSHCNs.

Methods

Population and Data Collection

This study used data collected from parents/caregivers of CSHCNs enrolled in the Florida Medicaid program between April 2006 and March 2007. These children were also eligible for admission into Florida’s integrated pediatric palliative care program. This program operates through a 1,115 Waiver from the Centers for Medicare and Medicaid Services and was designed for children to receive curative and life-prolonging care while also receiving palliative care, among other services. Based on the World Health Organization’s definition, pediatric palliative care emphasizes issues related to the child’s body, mind, and spirit, and involves family support to address the child’s physical, psychological, and social distress and symptoms. This principle applies to all pediatric chronic disorders (Sepulveda, Marlin, Yoshida, & Ullrich, 2002). All children who participated in this study had life-threatening conditions, but none were at the end of life at the time this study was conducted. These children were receiving active treatment combined with supportive therapies (e.g., music therapy, plan therapy, and family counseling) for their conditions; they were not enrolled in hospice or end-of-life care.

We identified a random sample of CSHCNs who met the eligibility criteria to be enrolled in the palliative care waiver program from the database housed in the Institute for Child Health Policy at University of Florida. The inclusion criteria included children who were between 2 and 18 years old and enrolled in Medicaid ≥6 months, and parents/caregivers who were ≥18 years old and were able to listen and speak English. For each potential participant, 10 attempts of phone calls were conducted. After the University of Florida’s Institutional Review Board approved the protocol, we sent an introductory letter to a random sample of 936 parents/caregivers whose children met our selection criteria, followed by a telephone interview between November 2007 and April 2008. Among 936 parents/caregivers, we were able to access 447 who had valid contact information; of the accessible group, 266 agreed to participate and completed the informed consent (response rate 59.5%). The parent/caregiver who was the most knowledgeable about the child’s health and health care was interviewed. A $25 gift card was provided for families who completed the survey. There was no difference in child’s age and health status between eligible participants who did or did not take part in this study (p > .05).

This study focuses on children with varying diagnoses who are considered to have special health care needs. We categorized the children into health status groups using the Clinical Risk Groups (CRGs) for two reasons. First, CSHCNs often share commonalities related to problems with daily functioning that cut across different diagnoses (Bethell, Read, Blumberg, & Newacheck, 2008). Second, many CCs in childhood are relatively rare, particularly those conditions that qualify a child for the Florida Medicaid integrated palliative care program.

The CRGs is an International Classification of Diseases, Ninth Revision-based system that uses claim and encounter data to classify children into one of five mutually exclusive categories. These categories represent the intensity of the child’s health status during a 12-month period immediately preceding the survey; children with major CCs have more severe needs than those with moderate CCs. These five categories include: (1) nonsignificant, nonacute: children whose underlying CC was not recorded in the claims data but were seen for routine care; (2) significant acute conditions: children with acute illnesses that could be precursors to or place the child at risk for developing a chronic disease (e.g., traumatic brain injury); (3) minor CCs: children with illnesses that can usually be managed effectively with few complications (e.g., attention deficit/hyperactivity disorder); (4) moderate CCs: children with illnesses that are variable in their severity and progression, can be complicated, and require extensive care (e.g., diabetes); and (5) major CCs: children with illnesses that are serious, and often result in progressive deterioration, debility, or death (e.g., cystic fibrosis) (Neff et al., 2002).

Measurement and Instruments

Three well-validated instruments the PedsQL MFS (Varni et al., 2002), the PedsQL Generic Core Scale (GCS) (Varni, Seid, & Kurtin, 2001), and the Impact on Family Scale (IFS) (Stein & Jessop, 2003) were used to measure pediatric fatigue, pediatric HRQOL, and family impact, respectively. Parent proxy-reports were used because evidence suggests many CSHCNs may be too sick to answer the survey, and parents’ observation on children’s HRQOL is a major impetus toward seeking health care for their children and helps make treatment decisions (Huang, Wen, Revicki, & Shenkman, 2011).

The PedsQL MFS measures fatigue in pediatric patients aged 2–18 years (Varni et al., 2002). This instrument is composed of 18 items to capture three domains of fatigue symptoms: general fatigue, sleep/rest fatigue, and cognitive fatigue. Domain scores are calculated by summing item scores in a specific domain and linearly transforming to 0–100, where 0 indicates the lowest fatigue and 100 the highest. The PedsQL GCS measures generic HRQOL in pediatric patients aged 2–18 years. This instrument is composed of 21/23 items (see later in the text) to capture four domains of HRQOL: physical, emotional, social, and school functioning (Varni et al., 2001). The school functioning includes three items for the 2–4 years age-group and five items for the 5–18 years age-group. Domain scores are calculated by summing item scores in a specific domain and linearly transforming to 0–100, where 0 indicates the lowest HRQOL and 100 the highest. The IFS measures the perceived burden that a child with CCs has on the social and personal strain of the families, and a 15-item short version was used to measure family impact (Stein & Jessop, 2003). The total IFS score is calculated by summing the item scores, where 0 indicates the lowest family impact and 100 the highest.

Statistical Analysis

First, descriptive analyses, including mean and standard deviation, were performed to investigate the distribution of individual domain scores on the three instruments. Cronbach's alpha coefficients and inter-item reliability with an overlap adjustment were estimated to evaluate the reliability of the instruments. To determine the level of impairment on pediatric fatigue, HRQOL, and family impact, domain scores of our study sample were compared with a general children population of a previous study (Varni, Limbers, & Burwinkle, 2007).

We then conducted correlation and regression analyses to investigate the relationships among pediatric fatigue, pediatric HRQOL, and family impact. Bivariate relationships between the domains of fatigue, HRQOL, and family impact were examined using Pearson correlation coefficients. Multivariate relationships were examined using three linear regression models with the family impact as the outcome. Model 1 includes three fatigue domains; Model 2 includes four HRQOL domains; and Model 3 includes three fatigue domains and four HRQOL domains. We recognize fatigue problems in younger children might have different implications for HRQOL outcomes compared with fatigue problems in older children. We therefore adjusted for children’s age in each model to mitigate the potential confounding effect. In addition, we adjusted for child’s gender and health conditions based on CRGs, and parents’/caregivers’ age, race/ethnicity, education, and marital status in each model.

Finally, we conducted path analyses to investigate the direct effect of the three domains of pediatric fatigue on family functioning, and the indirect effect of pediatric fatigue on family functioning through four domains of pediatric HRQOL as mediators. Structural equation modeling (SEM) was used to test the direct and indirect effects of fatigue on family impact through pediatric HRQOL. The use of SEM allows a simultaneous estimation of regression coefficients for the hypothesized pathways among the domains of interest. In SEM, we assume the constructs for the domains of interests (fatigue, HRQOL, family impact) are latent and cannot be measured directly; however, they can be estimated indirectly through the observed variables (i.e., items in the questionnaires). Two SEMs were performed: the full and recued models. The full SEM tests all pathways among the domains of fatigues, HRQOL, and family impact. In contrast, the reduced SEM tests the pathways among the domains that are statistically significant in the full model. In both SEM models, we adjusted for the same covariates as in the multivariate analyses. We estimated the root mean square error of approximation (RMSEA) to suggest the goodness of model fit. The range of RMSEA is between 0 and 1, and the value <.06 is regarded as an adequate model fit (Hu & Bentler, 1999). We used LISREL to perform path analyses (Mels, 2006) and STATA 10.0 for the rest of the analyses.

Results

Subject Characteristics

Table I shows subject characteristics. Of the 266 parents/caregivers who completed the survey, the mean age was 43 years (SD: 12). The majority of the parents/caregivers were white (41%). The mean age of children was 12 years (SD: 6), and 45% were boys. Based on the CRGs, 73% of children were classified with major CCs, 22% with moderate conditions, and 5% with minor conditions.

Table I.

Subject Characteristics (n = 266)

Characteristics Mean (SD) or %
Child’s age (years) 11.5 (5.50)
Child’s gender, %
    Boy 44.7%
Parent’s age (years) 42.9 (11.9)
Parent’s education background, %
    Below high school 22.2%
    High school or general educational development (GED) test 35.0%
    Some college 22.2%
    Associate degree or above 20.7%
Parent’s race/Ethnicity, %
    Hispanic 29.7%
    White/Non-Hispanic 41.4%
    Black/Non-Hispanic 24.4%
    Other 4.5%
Parent’s marital status, %
    Married/Common law 48.5%
    Divorced/Separated 24.2%
    Single 25.2%
    Widowed 2.3%
Child’s health status—CRGs, %
    Minor chronic conditions 5.2%
    Moderate chronic conditions 21.9%
    Major chronic conditions 72.8%
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Measurement of Pediatric Fatigue, HRQOL, and Family Impact

Table II shows the domain scores for pediatric fatigue, pediatric HRQOL, and family impact measures. All domain scores were slightly skewed to poorer outcomes, except cognitive fatigue, school functioning, and family impact. Reliability of three instruments was acceptable (Fayers & Machin, 2007) with Cronbach's alpha coefficient >0.7, and inter-item correlations between 0.3 and 0.7 for domain of the PedsQL GCS, and 0.5 and 0.8 for domain of the PedsQL MFS. In addition, Table II shows that CSHCNs had greater fatigue and impaired HRQOL in all domains compared with a previous study focusing on a general children population (p < .001).

Table II.

Domain Scores of Fatigue, HRQOL, and Family Impact Measures

Measures CSHCNs in this study
 Healthy childrena
Mean (SD) Floor effectb, % Ceiling effectb, % Cronbach's α Mean (SD)
PedsQL MFSc
    General fatigue 41.36 (25.29) 7.39 3.48 0.85 10.70 (13.33)
    Sleep/rest fatigue 33.57 (24.29) 10.87 1.3 0.84 11.14 (14.72)
    Cognitive fatigue 48.57 (30.72) 9.78 10.22 0.92 9.28 (15.15)
PedsQL GCSd
    Physical functioning 52.55 (28.27) 2.64 3.52 0.85 84.48(19.51)
    Emotional functioning 65.53 (22.35) 9.78 0.89 0.74 81.31 (16.50)
    Social functioning 58.13 (24.59) 4.39 5.26 0.70 83.70 (19.43)
    School functioning 52.21 (24.84) 0.45 3.48 0.72 78.83 (19.59)
IFSc
    Family impact 51.87 (18.73) 0 0 0.89 N/A
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Note. MFS = Multidimensional Fatigue Scale; GCS = Generic Core Scale; IFS = Impact on Family Scale.

bFloor and ceiling effects as the percentage of subjects with the lowest and highest scores, respectively, for a specific domain of the instruments.

cHigher scores, greater fatigue and family impact.

dHigher scores, better HRQOL.

Bivariate Association Between Pediatric Fatigue, HRQOL, and Family Impact

Table III shows the pairwise comparisons on the Pearson correlation coefficients across all domains of fatigue, HRQOL, and family impact measures. In general, the three measures were moderately correlated. Correlation coefficients of general fatigue and sleep/rest fatigue with emotional HRQOL were r = −.57 (p < .001) and r = −.58 (p < .001), respectively. In comparison, correlation coefficients of general fatigue and sleep/rest fatigue with social and school HRQOL were between −.33 and −.49 (p < .001). In addition, general fatigue was moderately associated with physical HRQOL, r = −.53 (p < .001), and cognitive fatigue was moderately associated with school HRQOL, r = −.60 (p < .001). Correlation coefficients for family impact and three fatigue domains were significant, r = .33 to .40 (p < .001); correlation coefficients for family impact and four HRQOL domains were also significant, r = −.29 to −.45 (p < .001).

Table III.

Bivariate Analyses: Correlations Between Fatigue, HRQOL, and Family Impact Measures

Measures PedsQL MFS
 PedsQL GCS
General fatigue Sleep/Rest fatigue Cognitive fatigue Physical functioning Emotional functioning Social functioning School functioning
Sleep/Rest fatigue .75*
Cognitive fatigue .44* .31*
Physical functioning −.53* −.36* −.42*
Emotional functioning −.57* −.58* −.42* .42*
Social functioning −.47* −.33* −.41* .50* .53*
School functioning −.49* −.43* −.60* .36* .44* .34*
Family impact .40* .34* .33* −.42* −.45* −.29* −.35*
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Note. MFS = Multidimensional Fatigue Scale; GCS = Generic Core Scale.

*p < .001.

Multivariate Association Between Pediatric Fatigue, HRQOL, and Family Impact

Table IV shows the dynamic relationships between pediatric fatigue, HRQOL, and family impact based on sequential regression models. Model 1 reveals more general fatigue and cognitive fatigue were associated with higher family impact (p < .05 and p < .01, respectively). Model 2 reveals higher physical and emotional functioning were associated with less family impact (p < .001). However, Model 3, which includes both fatigue and HRQOL variables, indicates the previously significant associations of fatigue with family impact were diluted (p > .05) and only higher physical and emotional functioning were associated with less family impact (p < .001).

Table IV.

Multivariate Analyses: Effects of Fatigue and HRQOL on Family Impacta

Measures Model 1
 Model 2
 Model 3
Coefficient (SE) 95% CI Coefficient (SE) 95% CI Coefficient (SE) 95% CI
PedsQL MFS
    General fatigue .12 (.07)* .02, .26 – – .00 (.07) −.14, .14
    Sleep/rest fatigue .08 (.07) −.05, .22 – – .02 (.07) −.12, .16
    Cognitive fatigue .15 (.04)** .07, .23 – – .04 (.05) −.05, .13
PedsQL GCS
    Physical functioning – – −.19 (.05)*** −.28, −.10 −.19 (.05)*** −.29, −.09
    Emotional functioning – – −.22 (.06)*** −.34, −.10 −.20 (.07)*** −.33, −.07
    Social functioning – – .01 (.06) −.10, .12 −.01 (.06) −.12, .11
    School functioning – – −.06 (.05) −.16, .04 −.02 (.06) −.14, .10
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Note. MFS = Multidimensional Fatigue Scale; GCS = Generic Core Scale.

aAdjust for child’s age, gender, and health conditions, as well as parents’ age, race/ethnicity, educational background, and marital status.

*p < .05; **p < .01; ***p < .001.

Specific Effect on the Pathway

Path analyses reveal that greater fatigue in any of the domains was not directly associated with greater family impact (p > .05). Instead, greater fatigue was associated with greater family impact through impairment in specific domains of HRQOL. In the full SEM, general fatigue was significantly associated with impaired physical (p < .001), emotional (p < .001), and social (p < .01) HRQOL. Sleep/rest fatigue was significantly associated with impaired emotional HRQOL (p < .05). Cognitive fatigue was significantly associated with impaired physical (p < .001), emotional (p < .001), social (p < .01), and school (p < .001) HRQOL. Impairment in physical and emotional HRQOL was significantly associated with greater family impact (p < .001).

Figure 1 shows the reduced SEM that only includes significant pathways generated from the full SEM. The model fit was satisfied with RMSEA = .05. Five pathways were identified, suggesting the effects of specific fatigue domains on family impact act through the specific HRQOL domains. The first pathway is the general fatigue on family impact through physical HRQOL (magnitude: .09; p < .001); the second pathway is the general fatigue on family impact through emotional HRQOL (magnitude: .19; p < .001); the third pathway is the sleep/rest fatigue on family impact through emotional HRQOL (magnitude: .11; p < .001); the fourth pathway is the cognitive fatigue on family impact through physical HRQOL (magnitude: .12; p < .001); and the fifth pathway is the cognitive fatigue on family impact through emotional HRQOL (magnitude: .10; p < .001). The magnitude of total indirect effects was .62 (p < .001).

Figure 1.

Figure 1.

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Effects of fatigue on family impact through HRQOL: a path analysis.

Discussion

CSHCNs place many demands on the families. It is critical to identify important and manageable risk factors related to family impact. Although CSHCNs report considerable fatigue, poor HRQOL, and impaired family functioning, the complex relationships among fatigue, HRQOL, and family impact are not well-understood. Previous studies often assume that fatigue plays a mediating role in the relationship between somatic symptoms and HRQOL. For example, fatigue significantly mediates the role of pain on children’s school functioning (Berrin et al., 2007). Few studies link fatigue to the burden placed on family, especially in pediatric populations.

Our results indicate CSHCNs had greater fatigue and impaired HRQOL in all domains as compared with a general children population (Varni et al., 2002, 2004, 2007). We extend the existing literature (Gibson et al., 2005) about the relationship between pediatric fatigue and family functioning and suggest the relationship is not straightforward. In multivariate analyses, we found greater pediatric fatigue (especially general and cognitive fatigue) and impaired HRQOL (especially physical and emotional functioning) were associated with greater family impact. However, the association between fatigue and family impact was attenuated by including HRQOL domains in the model. Of note, although IFS captures the concept of fatigue in one item “fatigue is a problem,” the association of fatigue with family impact was the same after this item was removed from the analyses. In path analyses, we identified five pathways, suggesting greater pediatric fatigue was associated with greater family impact through impaired pediatric physical and emotional HRQOL rather than social and school HRQOL.

This study supports that children’s fatigue may serve as a warning sign for a broader issue associated with daily functioning, such as physical and emotional HRQOL, and which in turn threatens family functioning. It is self-evident that fatigue is a symptom and has strong and direct effects on the child himself/herself rather than on family members. However, family members will greatly experience the effect of the child’s fatigue if fatigue impairs the child’s daily activities and HRQOL, typically physical and mental functioning, and creates more health care needs. A previous qualitative study focusing on fatigue issues of children with cancer suggests that these patients have a great tendency to lie in bed, engage in fewer physical activities, and sleep frequently, preventing them from maintaining normal daily routines. This alteration in their daily routines causes conflict between children and their parents and also disrupts their parents’ daily lives (Chiang, Yeh, Wang, & Yang, 2009). This argument is supported by our finding in the significant “fatigue–physical/emotional HRQOL–family impact” pathways rather than “fatigue–social/school HRQOL–family impact” pathways. Literature suggests that children’s physical and emotional well-being, rather than social and school activities, is more likely to be perceived by parents and caregivers (Eiser & Morse, 2001).

Should we increase our clinical attention to the fatigue issue because it is a common symptom among CSHCNs or children with CCs (Meeske et al., 2007; Wolfe et al., 2000), and contributes to an adverse impact on the family? The answer is definitely yes. Unfortunately, clinicians usually underdiagnose and undertreat different types of fatigue. One study suggests that fatigue was treated for <20% of children with advanced cancer, lower than other symptoms such as pain (80%) and dyspnea (75%) (Wolfe et al., 2000). This might be owing to the fact that clinicians are unaware of the association between problematic fatigue and poor daily functioning in the families, or it could be owing to the limited interventions available to clinicians to address this important issue. In addition, fatigue and depression are clustering symptoms. It is important to screen for and treat fatigue because fatigue can both contribute to depression and/or be a symptom of depression (Zebrack et al., 2002).

Several behavioral, complementary therapy, exercise, and pharmacological approaches have shown some promise in the management of fatigue symptoms (Radbruch et al., 2008). To reduce family impact, clinicians should screen CSHCNs not only for the causes and symptoms of fatigue (sleep disturbances, opioid-related sedation, and physical symptoms of nausea, etc.), but also for the problems in daily functioning and HRQOL. Our path analysis specifically suggests that physical and emotional functioning has a potential for being actionable. Several physical training and psychosocial interventions can be used to maintain children’s daily functioning to reduce the family impact (Osborn, Demoncada, & Feuerstein, 2006). However, most of these interventions are available for adults with CCs (Jacobsen & Jim, 2008) rather than children. More research is needed to identify efficacious strategies to address pediatric fatigue issues, to help clinicians understand the effect of fatigue on HRQOL and family functioning, and to investigate the complex associations of pediatric fatigue, depression, and other symptoms with HRQOL and family impact.

Several study limitations merit attention. First, the generalizability of our findings to the overall population of CSHCNs is limited because the underlying characteristics of our Medicaid children and their parents/caregivers may be different from children and parents/caregivers with other socioeconomic backgrounds. Second, we focused on the pathways in which fatigue affects family functioning through HRQOL, but did not account for the influence of other psychosocial variables, such as social support and coping. Third, we relied on parents rather than children to report pediatric fatigue symptoms and HRQOL. Fatigue and HRQOL are self-referenced phenomena, and the parent report may not truly reflect the child’s fatigue and HRQOL. Literature consistently suggests that agreement between the parent and child ratings of pediatric HRQOL is poor, and the lack of agreement might be owing to differences in perceived constructs of HRQOL or item meanings (Huang et al., 2009). However, as described in the Methods, parental ratings of pediatric fatigue and HRQOL provide unique aspect of the child’s heath status and influence treatment decisions. Fourth, we classified children’s health status based on CRGs rather than disease diagnoses. The limitations of using CRGs is that the specific issues such as fatigue related to a particular diagnosis and unique aspects of the disease in terms of treatment and progression cannot be identified. Within each of these categories, there is likely variability related to the particular disease within that category and its severity. However, CSHCNs share common functional limitations that cut across specific diagnoses. By using the CRGs, children can be aggregated into meaningful health status groupings based on the intensity of health needs to allow for analyses that might otherwise be impossible without large multicenter studies owing to the small number of children within each of the diagnoses that comprise the category of life-threatening conditions. Finally, the fatigue–HRQOL–family impact pathway was tested based on a cross-sectional design, which cannot be interpreted as a causal relationship. Future studies are encouraged to replicate our findings using alternative designs.

In conclusion, this is one of few studies to demonstrate the relationship among fatigue, HRQOL, and family impact in CSHCNs. Although fatigue is associated with impaired family functioning, the effect is through the mechanism of impaired physical and emotional HRQOL. This study sheds additional light on the importance of screening and managing the issues of fatigue and HRQOL and hopefully will reduce the impact on family functioning among CSHCNs.

Funding

This work was supported in part by the National Institute of Health (grant # K23 HD057146 and U01 AR052181) and Florida Agency for Health Care Administration (grant # HO7173).

Conflicts of interest: None declared.

References

  1. Berrin S J, Malcarne V L, Varni J W, Burwinkle T M, Sherman S A, Artavia K, Chambers H G. Pain, fatigue, and school functioning in children with cerebral palsy: A path-analytic model. Journal of Pediatric Psychology. 2007;32:330–337. doi: 10.1093/jpepsy/jsl017. doi:10.1093/jpepsy/jsl017. [DOI] [PubMed] [Google Scholar]
  2. Bethell C D, Read D, Blumberg S J, Newacheck P W. What is the prevalence of children with special health care needs? Toward an understanding of variations in findings and methods across three national surveys. Maternal and Child Health Journal. 2008;12:1–14. doi: 10.1007/s10995-007-0220-5. doi:10.1007/s10995-007-0220-5. [DOI] [PubMed] [Google Scholar]
  3. Chiang Y C, Yeh C H, Wang K W, Yang C P. The experience of cancer-related fatigue in Taiwanese children. European Journal of Cancer Care. 2009;18(1):43–49. doi: 10.1111/j.1365-2354.2007.00884.x. doi:10.1111/j.1365-2354.2007.00884.x. [DOI] [PubMed] [Google Scholar]
  4. Dodge J A, Morison S, Lewis P A, Coles E C, Geddes D, Russell G, Littlewood J, Scott M T. Incidence, population, and survival of cystic fibrosis in the UK, 1968-95. UK cystic fibrosis survey management committee. Archives of Disease in Childhood. 1997;77:493–496. doi: 10.1136/adc.77.6.493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Eddy L, Cruz M. The relationship between fatigue and quality of life in children with chronic health problems: A systematic review. Journal for Specialists in Pediatric Nursing. 2007;12:105–114. doi: 10.1111/j.1744-6155.2007.00099.x. doi:JSPN099 [pii]; 10.1111/j.1744-6155.2007.00099.x [doi] [DOI] [PubMed] [Google Scholar]
  6. Edwards J L, Gibson F, Richardson A, Sepion B, Ream E. Fatigue in adolescents with and following a cancer diagnosis: Developing an evidence base for practice. European Journal of Cancer. 2003;39:2671–2680. doi: 10.1016/j.ejca.2003.09.007. [DOI] [PubMed] [Google Scholar]
  7. Eiser C, Morse R. Can parents rate their child's health-related quality of life? Results of a systematic review. Quality of Life Research. 2001;10:347–357. doi: 10.1023/a:1012253723272. [DOI] [PubMed] [Google Scholar]
  8. Fayers P M, Machin D. Quality of life: The assessment, analysis, and interpretation of patient-reported outcomes. 2nd ed. 2007. Chichester, UK; Hoboken, NJ: J. Wiley. [Google Scholar]
  9. Garralda M E, Rangel L. Impairment and coping in children and adolescents with chronic fatigue syndrome: A comparative study with other paediatric disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines. 2004;45:543–552. doi: 10.1111/j.1469-7610.2004.00244.x. [DOI] [PubMed] [Google Scholar]
  10. Gibson F, Garnett M, Richardson A, Edwards J, Sepion B. Heavy to carry: A survey of parents' and healthcare professionals' perceptions of cancer-related fatigue in children and young people. Cancer Nursing. 2005;28:27–35. doi: 10.1097/00002820-200501000-00004. [DOI] [PubMed] [Google Scholar]
  11. Groothoff J W. Long-term outcomes of children with end-stage renal disease. Pediatric Nephrology. 2005;20:849–853. doi: 10.1007/s00467-005-1878-9. doi:10.1007/s00467-005-1878-9. [DOI] [PubMed] [Google Scholar]
  12. Hu L, Bentler B M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55. [Google Scholar]
  13. Huang I C, Shenkman E A, Leite W, Knapp C A, Thompson L A, Revicki D A. Agreement was not found in adolescents' quality of life rated by parents and adolescents. Journal of Clinical Epidemiology. 2009;62:337–346. doi: 10.1016/j.jclinepi.2008.06.012. doi:10.1016/j.jclinepi.2008.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Huang I C, Wen P S, Revicki D A, Shenkman E A. Quality of life measurement for children with life-threatening conditions: Limitations and a new framework. Child Indicators Research. 2011;4:145–160. doi: 10.1007/s12187-010-9079-x. doi:10.1007/s12187-010-9079-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Iobst E A, Nabors L A, Brunner H I, Precht B. Pain, fatigue, family functioning, and attitude toward illness in children with juvenile rheumatic diseases. Journal of Developmental and Physical Disabilities. 2007;19:135–144. doi:10.1007/s10882-006-9028-2. [Google Scholar]
  16. Jacobsen P B, Jim H S. Psychosocial interventions for anxiety and depression in adult cancer patients: Achievements and challenges. CA: A Cancer Journal for Clinicians. 2008;58:214–230. doi: 10.3322/CA.2008.0003. doi:10.3322/CA.2008.0003. [DOI] [PubMed] [Google Scholar]
  17. Jastrowski Mano K E, Khan K A, Ladwig R J, Weisman S J. The impact of pediatric chronic pain on parents' health-related quality of life and family functioning: Reliability and validity of the PedsQL 4.0 family impact module. Journal of Pediatric Psychology. 2011;36:517–527. doi: 10.1093/jpepsy/jsp099. doi:10.1093/jpepsy/jsp099. [DOI] [PubMed] [Google Scholar]
  18. McPherson M, Arango P, Fox H, Lauver C, McManus M, Newacheck P W, Perrin J M, Shonkoff J P, Strickland B. A new definition of children with special health care needs. Pediatrics. 1998;102(1 Pt 1):137–140. doi: 10.1542/peds.102.1.137. [DOI] [PubMed] [Google Scholar]
  19. Meeske K A, Patel S K, Palmer S N, Nelson M B, Parow A M. Factors associated with health-related quality of life in pediatric cancer survivors. Pediatric Blood and Cancer. 2007;49:298–305. doi: 10.1002/pbc.20923. doi:10.1002/pbc.20923. [DOI] [PubMed] [Google Scholar]
  20. Mels G. LISREL for windows: Getting started guide. Lincolnwood, IL: Scientific Software International, Inc; 2006. [Google Scholar]
  21. Neff J M, Sharp V L, Muldoon J, Graham J, Popalisky J, Gay J C. Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system. Ambulatory Pediatrics. 2002;2:71–79. doi: 10.1367/1539-4409(2002)002<0071:iaccwc>2.0.co;2. [DOI] [PubMed] [Google Scholar]
  22. Osborn R L, Demoncada A C, Feuerstein M. Psychosocial interventions for depression, anxiety, and quality of life in cancer survivors: Meta-analyses. International Journal of Psychiatry in Medicine. 2006;36:13–34. doi: 10.2190/EUFN-RV1K-Y3TR-FK0L. [DOI] [PubMed] [Google Scholar]
  23. Radbruch L, Strasser F, Elsner F, Goncalves J F, Loge J, Kaasa S, Nauck F, Stone P, Research Steering Committee of the European Association for Palliative Care (EAPC) Fatigue in palliative care patients—an EAPC approach. Palliative Medicine. 2008;22:13–32. doi: 10.1177/0269216307085183. doi:10.1177/0269216307085183. [DOI] [PubMed] [Google Scholar]
  24. Ray L D. Parenting and childhood chronicity: Making visible the invisible work. Journal of Pediatric Nursing. 2002;17:424–438. doi: 10.1053/jpdn.2002.127172. doi:10.1053/jpdn.2002.127172. [DOI] [PubMed] [Google Scholar]
  25. Rhee H. Relationships between physical symptoms and pubertal development. Journal of Pediatric Health Care. 2005;19:95–103. doi: 10.1016/j.pedhc.2004.10.004. doi:10.1016/j.pedhc.2004.10.004. [DOI] [PubMed] [Google Scholar]
  26. Rhee H, Miles M S, Halpern C T, Holditch-Davis D. Prevalence of recurrent physical symptoms in U.S. adolescents. Pediatric Nursing. 2005;31:314–319, 350. [PubMed] [Google Scholar]
  27. Rimes K A, Goodman R, Hotopf M, Wessely S, Meltzer H, Chalder T. Incidence, prognosis, and risk factors for fatigue and chronic fatigue syndrome in adolescents: A prospective community study. Pediatrics. 2007;119:e603–e609. doi: 10.1542/peds.2006-2231. doi:10.1542/peds.2006-2231. [DOI] [PubMed] [Google Scholar]
  28. Sepulveda C, Marlin A, Yoshida T, Ullrich A. Palliative care: The world health organization's global perspective. Journal of Pain and Symptom Management. 2002;24:91–96. doi: 10.1016/s0885-3924(02)00440-2. [DOI] [PubMed] [Google Scholar]
  29. Stein R E, Jessop D J. The impact on family scale revisited: Further psychometric data. Journal of Developmental and Behavioral Pediatrics JDBP. 2003;24:9–16. [PubMed] [Google Scholar]
  30. Stiller C. Epidemiology of cancer in adolescents. Medical and Pediatric Oncology. 2002;39:149–155. doi: 10.1002/mpo.10142. doi:10.1002/mpo.10142. [DOI] [PubMed] [Google Scholar]
  31. van der Lee J H, Mokkink L B, Grootenhuis M A, Heymans H S, Offringa M. Definitions and measurement of chronic health conditions in childhood: A systematic review. JAMA. 2007;297:2741–2751. doi: 10.1001/jama.297.24.2741. doi:10.1001/jama.297.24.2741. [DOI] [PubMed] [Google Scholar]
  32. Varni J W, Burwinkle T M, Katz E R, Meeske K, Dickinson P. The PedsQL in pediatric cancer: Reliability and validity of the pediatric quality of life inventory generic core scales, multidimensional fatigue scale, and cancer module. Cancer. 2002;94:2090–2106. doi: 10.1002/cncr.10428. [DOI] [PubMed] [Google Scholar]
  33. Varni J W, Burwinkle T M, Szer I S. The PedsQL multidimensional fatigue scale in pediatric rheumatology: Reliability and validity. The Journal of Rheumatology. 2004;31:2494–2500. [PubMed] [Google Scholar]
  34. Varni J W, Limbers C A, Bryant W P, Wilson D P. The PedsQL multidimensional fatigue scale in type 1 diabetes: Feasibility, reliability, and validity. Pediatric Diabetes. 2009;10:321–328. doi: 10.1111/j.1399-5448.2008.00482.x. doi:10.1111/j.1399-5448.2008.00482.x. [DOI] [PubMed] [Google Scholar]
  35. Varni J W, Limbers C A, Burwinkle T M. Impaired health-related quality of life in children and adolescents with chronic conditions: A comparative analysis of 10 disease clusters and 33 disease categories/severities utilizing the PedsQL 4.0 generic core scales. Health and Quality of Life Outcomes. 2007;5 doi: 10.1186/1477-7525-5-43. doi:10.1186/1477-7525-5-43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Varni J W, Seid M, Kurtin P S. PedsQL 4.0: Reliability and validity of the pediatric quality of life inventory version 4.0 generic core scales in healthy and patient populations. Medical Care. 2001;39:800–812. doi: 10.1097/00005650-200108000-00006. [DOI] [PubMed] [Google Scholar]
  37. Wilson I B, Cleary P D. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273:59–65. [PubMed] [Google Scholar]
  38. Wolfe J, Grier H E, Klar N, Levin S B, Ellenbogen J M, Salem-Schatz S, Emanuel E J, Weeks J C. Symptoms and suffering at the end of life in children with cancer. The New England Journal of Medicine. 2000;342:326–333. doi: 10.1056/NEJM200002033420506. doi:10.1056/NEJM200002033420506. [DOI] [PubMed] [Google Scholar]
  39. Zebrack B J, Zeltzer L K, Whitton J, Mertens A C, Odom L, Berkow R, Robison L L. Psychological outcomes in long-term survivors of childhood leukemia, Hodgkin’s disease, and non-Hodgkin’s lymphoma: A report from the childhood cancer survivor study. Pediatrics. 2002;110(1 Pt 1):42–52. doi: 10.1542/peds.110.1.42. [DOI] [PubMed] [Google Scholar]

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