Secondary Logo

Journal Logo


Quality of Life and Its Predictors Among Children and Adolescents With Cancer

Pan, Hsien-Ting MD; Wu, Li-Min PhD, RN; Wen, Shu-Hui MSN, NP, RN

Author Information
doi: 10.1097/NCC.0000000000000433
  • Free


The 5-year survival rate for childhood cancer is now approximately 80% because of advancements in treatments, including combinations of surgery, chemotherapy, radiation, and bone marrow transplantation.1 Pediatric patients with cancer spend between 6 months and 3.5 years undergoing treatment, confronting their diseases and treatments while also dealing with developmental challenges. Two-thirds of childhood cancer survivors experience long-term adverse health effects from their illness or its treatment.2 Hence, long-term goals in the care and treatment for a child with cancer include not only curing the patient but also ensuring that the child will be resilient, with full functioning and optimal health-related quality of life (QoL).3,4

Health-related QoL is defined as a multidimensional assessment of physical, psychological, and social functioning and is sensitive to developmental changes in children and adolescents.5–7 Health-related QoL is increasingly being used as an outcome metric in clinical trials and QoL research,8 as well as to monitor the occurrence of sequelae in childhood cancer survivors.9 Medically related fatigue and learning difficulties can influence patients’ QoL.10,11 Fatigue, a subjective and multidimensional phenomenon,12,13 is the most distressing and prevalent treatment-related symptom experienced by patients from diagnosis through the completion of therapy, due to its persistence and capacity to interfere with many aspects of life.14–16 Pediatric patients with cancer often experience impaired physical performance,17 altered sleep patterns,18 and a reduced ability to talk or interact with others.19 Fatigue is also associated with depression.14

Previous studies have revealed inconsistent findings for factors potentially related to the QoL of pediatric patients with cancer, such as demographic and medical variables.11,20–27 Nevertheless, some interesting trends have emerged from these reports. First, poor prognosis, treatment status, and greater symptom prevalence11,23 seem to decrease the QoL of children with cancer significantly. Second, patients with central nervous system cancers report worse QoL than do leukemia survivors.11,22,25,26 Third, survivors given a diagnosis before adolescence report higher posttreatment QoL than do those given a diagnosis during adolescence.27 Fourth, age and sex predict QoL during treatment for pediatric patients with high-risk acute lymphoblastic leukemia (ALL).28 Admission days, days since diagnosis, female parent, and female sex of the patient have been identified as predictors of QoL during induction therapy in pediatric ALL.21 Other factors, such as the time since diagnosis, sex, age at diagnosis, and age at assessment, may influence QoL, although the corresponding evidence has been inconsistent.11,20,24

Previously identified determinants of QoL only were predictive for pediatric patients given a diagnosis of ALL. Given that QoL is an important concern in all pediatric patients with cancer, this study was designed to explore QoL and its predictors in children and adolescents with any cancer type. The objectives of this study were (1) to assess differences in QoL, distress behavior, and fatigue among three groups (7–12, 13–15, and 16–18 years old); (2) to examine the relationship of fatigue, distress behaviors, and a variety of demographic factors to QoL; and (3) to identify QoL predictors.

Conceptual Framework

Health-related QoL, an assessment designed to link clinical variables to patient outcome measures, provided the guiding theoretical framework for this study.5 Quality of life can be thought of as a continuum of increasing complexity in biological and physiological factors, symptom status, functional status, general health perceptions, and characteristics of the individual.5 In the current study, QoL was considered as an outcome measure. Fatigue was the most prevalent treatment-related symptom.14,15 Distress behaviors were considered as an individual’s perception of the level of distress in his/her daily life. Individual and biological variables that may influence QoL include age at diagnosis and assessment, sex, family structure, time since diagnosis, cancer types, treatment status (on/off), and relapse (Figure).

Conceptual model of quality of life and related variables.



A cross-sectional descriptive study design was used.

Setting, Participants, and Ethical Considerations

A research assistant recruited participants from the inpatient and outpatient pediatric hematology and oncology units of 2 medical teaching hospitals in Taiwan from 2012 through 2014. Potential participants were identified with the help of unit nurses and doctors. Inclusion criteria were as follows: (1) cancer diagnosis before 18 years old, (2) age between 7 and 18 years at the time of assessment, (3) undergoing treatment or completed treatment at the time of assessment, (4) ability and willingness to complete questionnaires, and (5) parents and patients both agreeing to participate. Exclusion criteria included the presence of developmental delay (eg, autism spectrum disorder, Down syndrome, etc) or major psychiatric illness (eg, schizophrenia, bipolar disorder, etc).

A trained research assistant provided comprehensive verbal and written information about the study to eligible recruits. After written informed consent had been obtained from participants and parents, participants were asked to complete questionnaires in a private, quiet environment. A research assistant conducted face-to-face interviews in inpatient or outpatient units. All data were de-identified. The study was approved by the institutional review board for medical centers in Taiwan. Participants were informed of their rights and could withdraw from the study at any time.



Scores on the Pediatric Quality of Life Inventory (PedsQL 4.0) served as the main dependent variable. The PedsQL 4.0 was designed to assess pediatric QoL in healthy children and patients aged 5 to 7, 8 to 12, or 13 to 18 years.8 The instrument contains 23 items that address 4 domains: physical functioning (8 items), emotional functioning (5 items), social functioning (5 items), and school functioning (5 items). Each item is rated on a 0 to 4 scale, where 0 indicates that the issue raised in the item is never a problem and 4 indicates that the issue is almost always a problem. Items were reverse scored and transformed to a 0 to 100 scale (ie, 0, 100; 1, 75; 2, 50; 3, 25; and 4, 0), with higher scores representing a better QoL. Reliability and validity of the PedsQL 4.0 have been demonstrated previously.29 In the current study, Cronbach’s α for PedsQL 4.0 was .90.


Demographic Questionnaire. Participants completed questionnaires that collected demographic data, including sex, age at the time of the interview, religious affiliation, highest educational level completed, family structure, cancer types, time since diagnosis, treatment status (on/off), and the presence or absence of relapse.

Distress Behaviors Scale. The Distress Behaviors Scale (DBS), developed in Taiwan in 1993,30 is a 53-item instrument used to assess distress levels related to daily life in children. For each item, the respondent is asked how much they agree or disagree with a sentence on a 1 to 4 scale, where 1, 2, 3, and 4 indicate strongly agree, agree, disagree, and strongly disagree, respectively.30 The DBS assesses 5 behavioral factors: self-concern, physical/psychological well-being, school life, relationships, and family life. Higher scores represent greater distress. Reliability and validity of the DBS have been confirmed previously.30 In the current study, Cronbach’s α for the DBS was .94.

Multidimensional Fatigue Scale. The Multidimensional Fatigue Scale (MFS) is an 18-item self-report instrument for use in children aged 5 to 7, 8 to 12, 13 to 18, or 18 to 25 years.31 The instrument is composed of 3 subscales: general fatigue (6 items; eg, “I feel tired”), sleep/rest fatigue (6 items; eg, “I spend a lot of time in bed”), and cognitive fatigue (6 items; eg, “It is hard for me to keep my attention on things”).31 Items were answered on a 5-point Likert scale, with 0 indicating never a problem to 4 indicating always a problem. Scores were transformed into a 0 to 100 scale (ie, 0, 100; 1, 75; 2, 50; 3, 25; 4, 0), with higher scores indicating less fatigue. Reliability and validity of the MFS have been demonstrated previously.32 In the current study, Cronbach’s α for the MFS was .91.

Data Analysis

All statistical analyses were performed in SPSS v.18.0 software (SPSS Institute, Chicago, Illinois). Before conducting the analyses, we examined data for missing data points, outliers, normality, and linearity. Demographic data were reported with descriptive statistics, including percentages, means, SDs, and ranges. One-way analysis of variance was used to examine differences in QoL, MFS, and DBS among groups. Spearman’s correlation coefficient (r) was used to determine relationships among variables (eg, scores from MFS and DBS and covariables). Quality of life predictors were examined by a multiple regression analysis. All variables, including QoL, fatigue, distress behaviors, and covariate variables (Figure), were examined for normality and homoscedasticity to avoid multicollinearity effects.


Sample Characteristics

A total of 150 pediatric cancer survivors participated in this study. The mean (SD) age at assessment was 13.3 (2.8) years. The demographic characteristics of study participants are summarized in Table 1. Briefly, more than half of all participants were boys. Most participants had a hematologic cancer diagnosis, were nonreligious, were living in a nuclear family structure, and had completed all treatments by the time of assessment. Most participants were in remission.

Table 1
Table 1:
QoL Index in Relation to Demographic Characteristics (N = 150)

QoL, DBS, and MFS

Analyses of QoL, DBS, and MFS are presented in Table 2. With regard to QoL, participants rated the lowest mean score on school functioning and the highest mean score on social functioning. School life distress was the most prominent domain on the DBS. In terms of the MFS, the lowest mean score (indicating the greatest fatigue) was obtained on the sleep/rest fatigue.

Table 2
Table 2:
Quality of Life, Distress Behaviors, and Fatigue

Generally, no statistically significant differences were found in mean scores on QoL, DBS, and MFS among the 3 age groups (7–12, 13–15, and 16–18 years), except for subscores on school functioning of PedsQL (F=3.10, P< .05) and general fatigue and sleep/rest fatigue of MFS (Fs = 3.65 and 4.16, respectively; all Ps < .05). Post hoc Scheffe analyses indicated that children (7–12 years) and adolescents (13–15 years) reported a better QoL in school functioning than those who were at ages 16 to 18 years. In addition, adolescents at ages 16 to 18 years experienced more general fatigue and sleep/rest fatigue than those who were 7 to 12 years old.

Relationships Among DBS, MFS, Demographic Factors, and QoL

Spearman’s correlation coefficients evaluating the relationships between QoL and related variables are summarized in Table 3. Quality of life scores were significantly and positively correlated with general fatigue scores (r=0.70), sleep/rest fatigue scores (r=0.55), cognitive fatigue scores (r=0.61), time since diagnosis (r=0.20), and family structure (r=0.27). Hence, less fatigue, greater time since diagnosis, living in nuclear family, and diagnosis at a younger age (r=−0.19, P< .05) were associated with a better QoL. In addition, participants reporting more distress behaviors such as self-concern distress, physical and psychological distress, school life distress, relationship distress, and family life distress (rs = −0.48, −0.45, −0.40, −0.44, and −0.21, respectively; all Ps < .01) had poor QoL. Quality of life was not significantly related to age at assessment, sex, and treatment status.

Table 3
Table 3:
Correlational Matrix Between Quality of Life and Variables (N = 150)

Predictors of QoL

To identify the significant predictors of QoL, we performed a stepwise multiple regression analysis, including variables that were significantly associated with QoL (ie, general fatigue, sleep/rest fatigue, cognitive fatigue, self-concern distress, physical and psychological distress, school life distress, relationship distress, family life distress, age at diagnosis, time since diagnosis, and family structure). Family structure was transformed to dummy variables (eg, nuclear family/reference group). General fatigue was the most significant variable in the model, accounting for 55% of the total variance in QoL. Four statistically significant predictors of QoL together accounted for 64% of the total variance (Table 4). The equation of QoL was as follows: QoL = 47.31 + 0.66 × (general fatigue) − 0.25 × (relationship distress) + 0.12 × (nuclear family) + 0.11 × (time since diagnosis).

Table 4
Table 4:
Stepwise Multiple Regression Analysis of Quality of Life


This study investigated QoL in children and adolescents with cancer duration and, after treatment, indicated that the lowest subscores were on school functioning, consistent with previous studies.31,33,34 Difficulty at school may be related to patients missing school during35 or after the completion of treatment.16 Returning to school and regular school attendance are thought to promote growth and development.36 A positive school experience is assumed to pave the way for successful integration into the adult workforce and the establishment of close interpersonal relationships.35 Barriers for a successful school reentry in pediatric patients with cancer are manifold and may include anxiety about peer teasing due to visible treatment effects, ongoing school absences, separation from peers, teachers’ overindulgence or unrealistic expectations regarding the abilities, illness-related disabilities (eg, impairments of attention, memory, and executive skills; fatigue; pain37), and lack of sufficient special needs support from teachers, peers, and families, as well as parental worries about their child becoming overwhelmed or exposed to infections.16,38 Healthcare professionals’ attitudes and provision of medical information to school personnel can ease school reentry.39,40 Healthcare providers can assist with reintegration by being aware of parents’ thoughts and patients’ individual school concerns and by promoting classroom and peer socialization during periods of extended absences and hospitalization.

Furthermore, our results first revealed that 16- to 18-year-old adolescents reported poor school functioning and experienced more general fatigue and sleep/rest fatigue. The reason might be that they are transiting from adolescents into young adults, and this period is one of the most difficult with the consequence of cancer and treatment affecting their body image, relationships, role models, future perspectives, and identification from social development and peers.41–43 Furthermore, fatigue could interfere many aspects of their life, such as impaired physical performance17 and reduced interaction with others.14,18 Hence, there is a need for a personalized care plan for this middle group of adolescents in managing school life and fatigue in the best ways.

Our study identified many factors that affect QoL in children and adolescents with cancer. In our findings, less fatigue, greater time since diagnosis, and diagnosis at a younger age were associated with a better QoL. In addition, fatigue was significantly related to QoL. These findings are similar to previous studies,18,21,22 which reported that fatigue was related negatively with physical and psychosocial QoL18,22 and a better QoL was associated with greater time since diagnosis of pediatric ALL.21 Furthermore, this finding is similar to that of another study, which found that children who were older at the time of assessment had a poorer QoL.21 However, sex, treatment status, and age at assessment did not have a relationship with QoL in this study. Generally, female sex has been significantly associated with an impaired QoL.11,21 Further research using a larger, more homogeneous cohort (in terms of age, cancer type, and treatment phase) is needed to confirm the role of sex in the QoL of pediatric patients with cancer.

Predictors of QoL

This study identified general fatigue as a significant predictor of QoL. Fatigue is a common source of distress and a prevalent physical symptom among pediatric patients with cancer, regardless of their treatment status.10,15,16,44 Impaired body functioning and physical performance have been reported both during and after childhood cancer treatment.45 However, the causes of these challenges are unclear, and no targeted therapies have been developed to alleviate them.46 Unfortunately, cancer-related fatigue in children is generally not addressed by healthcare providers or parents because, at least in part, of children’s limited abilities to communicate their concerns.47 Moreover, fatigue affects children and adolescents with cancer differently depending on age.19 Hence, to improve the QoL of pediatric patients with cancer, healthcare providers need to understand age-related differences in how cancer-related fatigue is experienced and determine effective strategies to help these children and adolescents manage their fatigue.

Relationship distress was another predictor of QoL in pediatric patients with cancer. Children and adolescents with cancer have more difficulties in peer relationships and prosocial behaviors than do their healthy peers.16,36,38 Pediatric patients with cancer may have experienced difficulties in interacting and forming relationships with peers because of their limited understanding of peer’s social rules and extended absenteeism.16,38 Vance and Eiser36 reported that childhood patients with cancer, aged 8 to 18 years, were 3 times as likely as their healthy peers to experience bullying at school48 and that children with cancer younger than 12 years were more sensitive and isolated than their healthy peers. Compounding the issue, poor self-esteem and social relationships can negatively affect academic performance.36,49 Hence, the care plan should include increasing social opportunities with peers, establishing a support network during treatment,50 and/or providing age-appropriate assessments to continue evaluating relationship distress during and after treatment.

Children and adolescents living in a nuclear family, a less frequently studied factor, had a better QoL. Simultaneously, living in a nuclear family was as a predictor for QoL in our study. Previous studies showed that family burden and stress can reduce children’s QoL,24,51 whereas healthy family functioning is associated with a better QoL.24 Single parents may experience stress because of social and economic disadvantages, a lack financial or emotional support, and their sole responsibility for both the technical and general caregiving tasks of their children with cancer.52 Hung and Chen53 reported that the nuclear family structure was associated with better family functioning in terms of communication and role fulfillment than the extended family structure. In general, children and adolescents living with parents have a better health, greater access to healthcare, and fewer emotional or behavioral problems than children living in other types of families.54 Compared with the western culture, the proportion of Asian children younger than 18 years living with 2 married parents was higher.54 In addition, the core value of family is responsibility for child-rearing in the Taiwanese society, and most married couples begin married life living with the husband’s parents and then later move out to establish a nuclear family.55 That may explain why this study revealed that family structure is a predictor of QoL in Taiwanese children and adolescents with cancer. Further research could pay attention to socioeconomic disparities and provide family intervention programs for those living in other family types.


The generalizability of this study may be limited by our inclusion and exclusion criteria. Specifically, we did not include patients who were younger than 7 years, were unable to complete the questionnaires, or had developmental delays or psychiatric diagnoses. Second, the subscale of physical functioning in the PedsQL encompasses fatigue. Thus, there is a challenge in interpreting the QoL score as QoL was partially defined as the absence of fatigue.

Conclusions and Implications

Pediatric patients with cancer reported school functioning as the lowest-rated QoL component. A better QoL was reported by patients with less fatigue and distress behaviors, as well as by those given a diagnosis of cancer at younger ages. Time since diagnosis and family structure significantly influenced QoL. General fatigue, relationship distress, living in a nuclear family, and greater time since diagnosis were predictors of QoL.

Healthcare providers should be particularly attentive to the child’s characteristics, especially focusing on patients who were given a diagnosis at an older age or who are living in single-parent or extended family structures. Additional support should be given around the time of diagnosis. Healthcare providers should develop age-appropriate intervention programs to enhance the management of fatigue and relationship distress, as well as school reintegration plans to prepare children and adolescents for returning to school.


The authors gratefully acknowledge the time and energy contributed by the participants.


1. Furlong W, Rae C, Feeny D, et al. Health-related quality of life among children with acute lymphoblastic leukemia. Pediatr Blood Cancer. 2012;59(4):717–724.
2. Ginsberg JP, Hobbie WL, Carlson CA, Meadows AT. Delivering long-term follow up care to pediatric cancer survivors: transitional care issue. Pediatr Blood Cancer. 2006;46(2):169–173.
3. Terracini B. Epidemiology of childhood cancer. Terracini Environ Health. 2011;10(suppl 1):1–3.
4. van Laar M, Glaser A, Phillips RS, Feltbower RG, Stark DP. The impact of a managed transition of care upon psychosocial characteristics and patient satisfaction in a cohort of adult survivors of childhood cancer. Psychooncology. 2013;22(9):2039–2045.
5. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. JAMA. 1995;273(1):59–65.
6. Chirivella S, Rajappa S, Sinha S, Eden T, Barr RD. Health-related quality of life among children with cancer in Hyderabad, India. Indian J Pediatr. 2009;76(12):1231–1235.
7. Langeveld NE, Stam H, Grootenhuis MA, Last BF. Quality of life in young adult survivors of childhood cancer. Support Care Cancer. 2002;10:579–600.
8. Ji Y, Chen S, Li K, et al. Measuring health-related quality of life in children with cancer living in Mainland China: feasibility, reliability and validity of the Chinese Mandarin version of PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module. Health Qual Life Outcomes. 2011;9:103.
9. Hinds PS, Burghen EA, Haase JE, Phillips CR. Advances in defining, conceptualizing, and measuring quality of life in pediatric patients with cancer. Oncol Nurs Forum. 2006;33(1):23–29.
10. Macartney G, Stacey D, Harrison MB, VanDenKerkhof E. Symptoms, coping, and quality of life in pediatric brain tumor survivors: a qualitative study. Oncol Nurs Forum. 2014;41(4):390–398.
11. Klassen AF, Anthony SJ, Khan A, Sung L, Klaassen R. Identifying determinants of quality of life of children with cancer and childhood cancer survivors: a systematic review. Support Care Cancer. 2011;19(9):1275–1287.
12. Gibson F, Mulhall AB, Richardson A, Edwards JL, Ream E, Sepion S. A phenomenologic study of fatigue in adolescents receiving treatment for cancer. Oncol Nurs Forum. 2005;32(3):651–660.
13. Hockenberry-Eaton M, Hinds P, O’Neill JB, et al. Developing a conceptual model for fatigue in children. Eur J Ontol Nurs. 1999;3(1):5–11.
14. Meeske KA, Siegel SE, Globe DR, Mack WJ, Bernstein L. Prevalence and correlates of fatigue in long-term survivors of childhood leukemia. J Clin Oncol. 2005;23(24):5501–5510.
15. Wang XS, Zhao F, Fisch MJ, et al. Prevalence and characteristics of moderate to severe fatigue: a multicenter study in cancer patients and survivors. Cancer. 2014;120(3):425–432.
16. McLoone JK, Wakefield CE, Cohn RJ. Childhood cancer survivors’ school (re)entry: Australian parents’ perceptions. Eur J Cancer Care. 2013;22(4):484–492.
17. Hooke MC, Garwick AW, Gross CR. Fatigue and physical performance in children and adolescents receiving chemotherapy. Oncol Nurs Forum. 2011;38(6):649–657.
18. Erickson JM, Beck SL, Christian BR, Dudley W, Dillon RL, Godder K. Fatigue, sleep-wake disturbances, and quality of life in adolescents receiving chemotherapy. J Pediatr Hematol Oncol. 2011;33(1):e17–e25.
19. Chiang YC, Yeh CH, Wang KW, Yang CP. The experience of cancer-related fatigue in Taiwanese children. Eur J Cancer Care. 2009;18(1):43–49.
20. Penn A, Lowis SP, Stevens MC, et al. Family, demographic and illness-related determinants of HRQL in children with brain tumours in the first year after diagnosis. Pediatr Blood Cancer. 2009;53(6):1092–1099.
21. Van Litsenburg RR, Huisman J, Pieters R, Verhaak C, Kaspers GJ, Gemke RJ. Determinants of quality of life during induction therapy in pediatric acute lymphoblastic leukemia. Support Care Cancer. 2014;22(12):3235–3242.
22. Meeske KA, Patel SK, Palmer SN, Nelson MB, Parow AM. Factors associated with health-related quality of life in pediatric cancer survivors. Pediatr Blood Cancer. 2007;49(3):298–305.
23. Huang IC, Brinkman TM, Kenzik K, et al. Association between the prevalence of symptoms and health-related quality of life in adult survivors of childhood cancer: a report from the St Jude Lifetime cohort study. J Clin Oncol. 2013;31(33):4242–4251.
24. Litzelman K, Barker E, Catrine K, Puccetti D, Possin P, Witt WP. Socioeconomic disparities in the quality of life in children with cancer or brain tumors: the mediating role of family factors. Psychooncology. 2013;22(5):1081–1088.
25. Bradley Eilertsen ME, Jozefiak T, Rannestad T, Indredavik MS, Vik T. Quality of life in children and adolescents surviving cancer. Eur J Oncol Nurs. 2012;16(2):185–193.
26. Cardarelli C, Cereda C, Masiero L, et al. Evaluation of health status and health-related quality of life in a cohort of Italian children following treatment for a primary brain tumor. Pediatr Blood Cancer. 2006;46(5):637–644.
27. Abu-Saad Huijer H, Sagherian K, Tamim H. Quality of life and symptom prevalence as reported by children with cancer in Lebanon. Eur J Oncol Nurs. 2013;17(6):704–710.
28. Sung L, Yanofsky R, Klaassen R, et al. Quality of life during active treatment for pediatric acute lymphoblastic leukemia. Int J Cancer. 2011;28(5):1213–1220.
29. Varni JW, Burwinkle TM, Seid M, Skarr D. The PEDSQL* 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3(6):329–341.
30. Li K-C, Ou H-M. Distress Behavior Scale (Fourth Edition) Guidebook. Taipei, Taiwan: Psychology Press; 2008.
31. Varni JW, Limbers CA, Bryant WP, Wilson DP. Assessment of fatigue in pediatric patients with short stature utilizing the PedsQL™ multidimensional fatigue scale. Child Health Care. 2012;41(2):162–181.
32. Kobayashi K, Okano Y, Hohashi N. Reliability and validity of the PedsQL multidimensional fatigue scale in Japan. Qual Life Res. 2011;20(7):1091–1102.
33. Ewing JE, King MT, Smith NF. Validation of modified forms of the PedsQL generic core scales and cancer module scales for adolescents and young adults (AYA) with cancer or a blood disorder. Qual Life Res. 2009;18(2):231–244.
34. Hao Y, Tian Q, Lu H, Chai Y, Rao S. Psychometric properties of the Chinese version of the pediatric quality of life inventory 4.0 generic core scales. Qual Life Res. 2010;19:1229–1233.
35. Prevatt FF, Heffer RW, Lowe PA. A review of school reintegration programs for children with cancer. J Sch Psychol. 2000;38(5):447–467.
36. Vance YH, Eiser C. The school experience of the child with cancer. Child Care Health Dev. 2002;28(1):5–19.
37. Cheung LL, Wakefield CE, Ellis SJ, Mandalis A, Frow E, Cohn RJ. Neuropsychology reports for childhood brain tumor survivors: implementation of recommendations at home and school. Pediatr Blood Cancer. 2014;61(6):1080–1087.
38. Upton P, Eiser C. School experiences after treatment for a brain tumour. Child Care Health Dev. 2006;32(1):9–17.
39. Selwood K, Hemsworth S, Rigg J. Children with cancer quality of information for returning to school. Nurs Child Young People. 2013;25(5):14–18.
40. Pini S, Hugh-Jones S, Gardner PH. What effect does a cancer diagnosis have on the educational engagement and school life of teenagers? A systematic review. Psychooncology. 2012;21(7):685–694.
41. Gupta AA, Papadakos JK, Jones JM, et al. Reimagining care for adolescent and young adult cancer programs: moving with the times. Cancer. 2016;122(7):1038–1046.
42. Nass SJ, Beaupin LK, Demark-Wahnefried W, et al. Identifying and addressing the needs of adolescents and young adults with cancer: summary of an institute of medicine workshop. Oncologist. 2015;20(2):186–197.
43. Warner EL, Kent EE, Trevino KM, Parsons HM, Zebrack BJ, Kirchhoff AC. Social well-being among adolescents and young adults with cancer: a systematic review. Cancer. 2016;1:1029–1037.
44. Litzelman K, Catrine K, Gangnon R, Witt WP. Quality of life among parents of children with cancer or brain tumors: the impact of child characteristics and parental psychosocial factors. Qual Life Res. 2011;20(8):1261–1269.
45. Hooke MC, Garwick AW, Neglia JP. Assessment of physical performance using the 6-minute walk test in children receiving treatment for cancer. Cancer Nurs. 2013;36(5):E9–E16.
46. Minton O, Richardson A, Sharpe M, Hotopf M, Stone P. A systematic review and meta-analysis of the pharmacological treatment of cancer-related fatigue. J Natl Cancer Inst. 2008;100:1155–1166.
47. Hockenberry-Eaton M, Hinds PS. Fatigue in children and adolescents with cancer: evolution of a program of study. Semin Oncol Nurs. 2000;16(4):261–272.
48. Lähteenmäki PM, Huostila J, Hinkka S, Salmi TT. Childhood cancer patients at school. Eur J Cancer. 2002;38(9):1227–1240.
49. Buizer AI, de Sonneville LM, van den Heuvel-Eibrink MM, Veerman AJ. Behavioral and educational limitations after chemotherapy for childhood acute lymphoblastic leukemia or Wilms tumor. Cancer. 2006;106(9):2067–2075.
50. Farjou G, Sinha R, Dix D, Shahbaz A, Klaassen RJ, Klassen AF. Understanding the healthcare experiences of teenaged cancer patients and survivors. Child Care Health Dev. 2014;40(5):723–730.
51. Roddenberry A, Renk K. Quality of life in pediatric cancer patients: the relationships among parents’ characteristics, children’s characteristics, and informant concordance. J Child Fam Stud. 2008;17(3):402–426.
52. Granek L, Rosenberg-Yunger ZRS, Dix D, et al. Caregiving, single parents and cumulative stresses when caring for a child with cancer. Child Care Health Dev. 2014;40(2):184–194.
53. Hung YL, Chen JY. Exploration of social support available to mothers of children with cancer, their health status, and other factors related to their family function. J Nurs. 2008;55(6):47–57.
54. Blackwell DL. Family structure and children’s health in the United States: findings from the National Health Interview Survey, 2001–2007. Vital Health Stat 10. 2010;10:246.
55. Chi PSK, Hsin PL. Family structure and fertility behavior in Taiwan. Popul Res Policy Rev. 1996;15:327–339.

Behavioral disturbance; Fatigue; Pediatric cancer; Quality of life

Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.