Top

Quality of Life Research

Published in:

Open Access 01-12-2016

Three-year trajectories of global perceived quality of life for youth with chronic health conditions

Authors: Janette McDougall, David J. DeWit, Megan Nichols, Linda Miller, F. Virginia Wright

Published in: Quality of Life Research | Issue 12/2016

Abstract

Purpose

Objectives of this longitudinal study were to examine 3-year trajectories of global perceived quality of life (QOL) for youth with chronic health conditions, as obtained from youth and parent reports, and to identify personal and environmental factors associated with the trajectory groups for each perspective.

Methods

Youth with various chronic conditions aged 11–17 years and one of their parents were recruited from eight children’s treatment centers. Latent class growth analysis was used to investigate perceived QOL trajectories (separately for youth and parent perspectives) over a 3-year period (four data collection time points spaced 12 months apart). Multinomial logistic regression was employed to identify factors associated with these trajectories.

Results

A total of 439 youth and one of their parents participated at baseline, and 302 (69 %) of those youth/parent dyads completed all four data collection time points. Two QOL trajectories were identified for the youth analysis: ‘high and stable’ (85.7 %) and ‘moderate/low and stable’ (14.3 %), while three trajectories were found for the parent analysis: ‘high and stable’ (35.7 %), ‘moderate and stable’ (46.6 %), and ‘moderate/low and stable’ (17.7 %). Relative to the ‘high and stable’ groups, youth with more reported pain/other physical symptoms, emotional symptoms, and home/community barriers were more likely to be in the ‘moderate and stable’ or ‘moderate/low and stable’ groups. Also, youth with higher reported self-determination, spirituality, family social support, family functioning, school productivity/engagement, and school belongingness/safety were less likely to be in the ‘moderate and stable’ or ‘moderate/low and stable’ groups, compared to the ‘high and stable’ groups.

Conclusion

Findings suggest that youth with chronic conditions experience stable global perceived QOL across time, but that some individuals maintain stability at moderate to moderate/low levels which is related to ongoing personal and environmental influences. Potential benefits of universal strategies and programs to safeguard resilience for all youth and targeted interventions to optimize certain youths’ global perceived QOL are indicated.

Available only for authorised users

World Health Organization. (2001). International classification of functioning, disability and health. Geneva: World Health Organization.

van der Lee, J., Mokkink, L., Grootenhuis, M., Heymans, H., & Offringa, M. (2007). Definitions and measurement of chronic health conditions in childhood: A systematic review. Journal of the American Medical Association, 297(24), 2741–2751.CrossRefPubMed

Michalos, A. (2006). The quality of life (QOL) research movement: Past, present, and future. Social Indicators Research, 76(3), 343–466.CrossRef

World Health Organization. (1946). Constitution of the World Health Organization: Chronicle of the World Health Organization 1. Geneva: World Health Organization.

Anderson, K., & Burckhardt, S. (1999). Conceptualization and measurement of quality of life as an outcome variable for health care intervention and research. Journal of Advanced Nursing, 29(Supplement 2), 298–306.CrossRefPubMed

Fayed, N., de Camargo, O., Kerr, E., Rosenbaum, P., Dubey, A., Bostan, C., et al. (2012). Generic patient-reported outcomes in child health: A review of conceptual content using the World Health Organization definitions. Developmental Medicine and Child Neurology, 54(12), 1085–1095.CrossRefPubMed

World Health Organization Quality of Life Group. (1998). The World Health Organization Quality of Life Assessment (WHO-QOL): Development and general psychometric properties. Social Science and Medicine, 46(12), 1569–1585.CrossRef

Huebner, E. S., Valois, R., Suldo, S., Smith, L., Mcknight, C., Seligson, J., & Zullig, K. (2004). Perceived quality of life: A neglected component of adolescent health assessment and intervention. Journal of Adolescent Health, 34(4), 270–278.CrossRefPubMed

Topolski, T., Edwards, T., & Patrick, D. (2004). Toward youth self-report of health and quality of life in population monitoring. Ambulatory Pediatrics, 4(4), 387–394.CrossRefPubMed

10.

Diener, E., Suh, E., Lucas, R., & Smith, H. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302.CrossRef

11.

World Health Organization Regional Office of Europe. (2013). Joint meeting of experts on targets and indicators for health and well-being in Health 2020. Copenhagen: World Health Organization Regional Office of Europe.

12.

McDougall, J., Baldwin, P., Evans, J., Nichols, M., Etherington, N., & Wright, V. (2016). Quality of life and self-determination: Youth with chronic health conditions make the connection. Applied Research in Quality of Life. 11(2), 571–599.CrossRefPubMed

13.

Tsonis, M., & McDougall, J. (2012). Interrelated processes toward quality of life in survivors of childhood cancer: A grounded theory. The Qualitative Report, 17(4), 1–18.

14.

Helseth, S., & Misvaer, N. (2010). Adolescents’ perceptions of quality of life: What it is and what matters. Journal of Clinical Nursing, 19(9–10), 1454–1461.CrossRefPubMed

15.

Beckie, T., & Hayduk, L. (1997). Measuring quality of life. Social Indicators Research, 42(1), 21–39.CrossRef

16.

Proctor, C., Linley, A., & Maltby, J. (2009). Youth life satisfaction: A review of the literature. Journal of Happiness Studies, 10(5), 583–630.CrossRef

17.

Fogle, L., Huebner, E. S., & Laughlin, J. (2002). The relationship between temperament and life satisfaction in early adolescence: Cognitive and behavioral mediation models. Journal of Happiness Studies, 3(4), 372–392.CrossRef

18.

Gilman, R. (2001). The relationship between life satisfaction, social interest, and frequency of extracurricular activities among adolescent students. Journal of Youth and Adolescence, 30(6), 749–767.CrossRef

19.

Huebner, E. S., Gilman, R., & Suldo, S. (2007). Assessing quality of life in children and youth. In S. Smith & L. Handler (Eds.), The clinical assessment of children and adolescents: A practitioner’s handbook (pp. 349–366). New York, NY: Taylor and Francis Group.

20.

Bradshaw, J., Marorano, B., Natili, L., & de Neubourg, C. (2013). Children’s subjective well-being in rich countries. Child Health Indicators, 6(4), 619–635.CrossRef

21.

Chong, J., Mackey, A., Broadbent, E., & Stott, S. (2012). Children’s perceptions of their cerebral palsy and their impact on life satisfaction. Disability and Rehabilitation, 34(24), 2053–2060.CrossRefPubMed

22.

Huebner, E. S. (1991). Initial development of the Student’s Life Satisfaction Scale. School Psychology International, 12(3), 231–240.CrossRef

23.

Emerson, E., Honey, A., Madden, R., & Llewellyn, G. (2009). The well-being of Australian adolescents and young adults with self-reported long-term health conditions, impairments, or disabilities: 2001 and 2006. Australian Journal of Social Issues, 44(1), 39–54.

24.

McDougall, J., Wright, V., DeWit, D., & Miller, L. (2014). ICF-based functional components and contextual factors as correlates of perceived quality of life for youth with chronic conditions. Disability and Rehabilitation, 36(25), 2143–2151.CrossRefPubMedPubMedCentral

25.

Chaves, C., Hervas, G., Garcia, F., & Vazquez, C. (2016). Building life satisfaction through well-being dimensions: A longitudinal study in children with a life-threatening illness. Journal of Happiness Studies, 17(3), 1051–1067.CrossRef

26.

Luyckx, K., Missotten, L., Goosens, E., & Moons, P. (2012). Individual and contextual determinants of quality of life in adolescents with congenital heart disease. Journal of Adolescent Health, 51(2), 122–128.CrossRefPubMed

27.

Luyckx, K., Goosens, E., Rassart, J., Apers, S., Vanhalst, J., & Moons, P. (2014). Parental support, internalizing problems, perceived health status, and quality of life in adolescents with congenital heart disease: Influences and reciprocal effects. Journal of Behavioral Medicine, 37(1), 145–155.CrossRefPubMed

28.

Emerson, E., Llewellyn, G., Honey, A., & Kariuki, M. (2012). Lower well-being of young Australian adults with self-reported disability reflects poorer living conditions rather than health issues. Australian and New Zealand Journal of Public Health, 36(2), 176–182.CrossRefPubMed

29.

Fernandes, L., Mendes, A., & Teixeira, C. (2012). A review essay on the measurement of child well-being. Social Indicators Research, 106(2), 239–257.CrossRef

30.

Eiser, C., & Morse, R. (2001). Can parents rate their child’s health-related quality of life? Results of a systematic review. Child: Care, Health and Development, 10(4), 347–357.

31.

Feldman, B., Grundland, B., McCullough, L., & Wright, V. (2000). Distinction of quality of life, health-related quality of life, and health status in children referred for rheumatologic care. The Journal of Rheumatology, 27(1), 226–233.PubMed

32.

Ferro, M., Camfield, C., Leven, S., Smith, M., Wiebe, S., Zou, G., & Speechley, K. (2013). Trajectories of health-related quality of life in children with epilepsy: A cohort study. Epilepsia, 54(11), 1897–2013.CrossRef

33.

Stein, R., & Jessop, D. (1989). What diagnosis does not tell: The case for a noncategorical approach to chronic illness in childhood. Social Science and Medicine, 29(6), 769–778.CrossRefPubMed

34.

Law, M., Finkleman, S., Hurley, P., Rosenbaum, P., King, S., King, G., & Hanna, S. (2004). Participation of children with physical disabilities: Relationships to diagnosis, physical function, and demographic variables. Scandinavian Journal of Occupational Therapy, 11(4), 156–162.CrossRef

35.

McDougall, J., Wright, V., & Rosenbaum, P. (2010). The ICF model of functioning and disability: Incorporating quality of life and human development. Developmental Neurorehabilitation, 13(3), 204–211.CrossRefPubMed

36.

Bijleveld, C., van der Kamp, L., Mooijaart, A., van der Kloot, W., van der Leeden, R., & van der Burg, E. (1998). Longitudinal data analysis: Designs, models, and methods. London: Sage.

37.

McDougall, J., Wright, V., Nichols, M., & Miller, L. (2013). Assessing the psychometric properties of both a global and domain-specific perceived quality of life measure when used with youth who have chronic conditions. Social Indicators Research, 114(3), 1243–1257.CrossRefPubMed

38.

Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581–586.CrossRefPubMed

39.

Bedell, G. (2004). Developing a follow-up survey focused on participation of children and youth with acquired brain injuries after discharge from inpatient rehabilitation. Developmental Neurorehabilitation, 19(3), 191–205.

40.

Seidlitz, L., Abernathy, A., Duberstein, P., Evinger, J., Change, T., & Lewis, B. (2002). Development of the Spirituality Index. Journal of the Scientific Study of Religion, 41(3), 439–453.CrossRef

41.

Wehmeyer, M., & Kelchner, K. (1995). The Arc’s Self-Determination Scale: Adolescent version. Arlington, TX: The Arc of the United States.

42.

Dubow, E., & Ullman, D. (1989). Assessing social support in elementary school children: The survey of children’s support. Journal of Clinical Child Psychology, 18(1), 52–64.CrossRef

43.

Statistics Canada. (1998). National Longitudinal Survey of Children and Youth: Survey instruments for 1994–95. Cat. no. 95-01. Ottawa, ON: Statistics Canada/Human Resources Development Canada.

44.

World Health Organization. (2006). The Health Behavior of School Aged Children Survey: 2005/2006. Geneva: World Health Organization.

45.

Muthen, B., & Muthen, L. (2010). M-plus. Statistical analysis with latent variables. User’s guide (6th ed.). Los Angeles, CA: Muthen & Muthen.

46.

Jung, T., & Wickrama, K. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317.CrossRef

47.

Nagin, D. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.CrossRef

48.

Twisk, J., & Hoekstra, T. (2012). Classifying developmental trajectories over time should be done with great caution: A comparison between methods. Journal of Clinical Epidemiology, 65(10), 1078–1087.CrossRefPubMed

49.

Raftery, A. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.CrossRef

50.

Wu, A., Zumbo, B., & Siegel, L. (2011). General piecewise growth mixture model: Word recognition development for different learners in different phases. Journal of Modern Applied Statistical Methods, 10(1), 226–248.

51.

Reinecke, J. (2006). Longitudinal analysis of adolescents’ deviant and delinquent behavior: Applications of latent class growth curves and growth mixture models. Methodology, 2(3), 100–112.CrossRef

52.

McDougall, J., Wright, V., Miller, L., Schmidt, J., & Lowry, K. (2011). Applying the ICF framework to study changes in quality of life for youth with chronic conditions. Developmental Neurorehabilitation, 14(1), 41–53.CrossRefPubMed

53.

Graham, J. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.CrossRefPubMed

54.

Heron, J., Hickman, M., Macleod, J., & Munafò, M. (2011). Characterizing patterns of smoking initiation in adolescence: Comparison of methods for dealing with missing data. Nicotine & Tobacco Research, 13(12), 1266–1275.CrossRef

55.

Hix-Small, H., Duncan, S., & Okut, H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment, 26(4), 255–270.CrossRef

56.

Li, F., Duncan, T. E., & Hops, H. (2001). Examining developmental trajectories in adolescent alcohol use using piecewise growth mixture modeling analysis. Journal of Studies on Alcohol, 62(2), 199–210.CrossRefPubMed

57.

Wang, M., & Bodner, T. (2007). Growth mixture modeling: Identifying and predicting unobserved subpopulations with longitudinal data. Organizational Research Methods, 10(4), 635–656.CrossRef

58.

Sterba, S., Prinstein, M., & Cox, M. (2007). Trajectories of internalizing problems across childhood: Heterogeneity, external validity, and gender differences. Development and Psychopathology, 19(2), 345–366.CrossRefPubMed

59.

King, G., Baldwin, P., Currie, M., & Evans, J. (2005). Planning successful transitions from school to adult roles for youth with disabilities. Children’s Health Care, 34(3), 193–216.CrossRef

60.

Van Leeuwen, C., Post, M., Hoekstra, T., van der Woude, L., de Groot, S., Snoek, G., et al. (2011). Trajectories in the course of life satisfaction after spinal cord injury: Identification and predictors. Archives of Physical Medicine and Rehabilitation, 92(2), 207–213.CrossRefPubMed

61.

Ahmed, S., Sawatszky, R., Levesque, J., Ehrmann-Feldman, D., & Schwartz, C. (2014). Minimal evidence of response shift in the absence of a catalyst. Quality of Life Research, 23(9), 2421–2430.CrossRefPubMed

62.

Upton, P., Lawford, J., & Eiser, C. (2008). Parent-child agreement across child health-related quality of life instruments: A review of the literature. Quality of Life Research, 17(6), 895–913.CrossRefPubMed

63.

Savage, A., McConnell, D., Emerson, E., & Llewellyn, G. (2014). Disability-based inequity in youth subjective well-being: Current findings and future directions. Disability & Society, 29(6), 877–892.CrossRef

64.

Cummins, R. (2010). Subjective well-being, homeostatically protected mood and depression: A synthesis. Journal of Happiness Studies, 11(1), 1–17.CrossRef

65.

Cummins, R., Li, L., Wooden, M., & Stokes, M. (2014). A demonstration of set-points for subjective wellbeing. Journal of Happiness Studies, 15(1), 183–206.CrossRef

66.

Cummins, R. (2016). The theory of subjective wellbeing homeostasis: A contribution to understanding life quality. In F. Maggini (Ed.), A life devoted to quality of life. Social Indicators Research Series, 60, 61-79.

67.

Tomyn, A., Weinberg, M., & Cummins, R. (2015). Intervention efficacy among ‘at risk’ adolescents: A test of subjective wellbeing Homeostasis Theory. Social Indicators Research, 120(3), 883–895.CrossRef

68.

Cummins, R., & Lau, A. (2005). Personal Wellbeing Index-School Children (PWI-SC) (3rd ed.). Melbourne: Deakin University.

69.

Newacheck, P., & Stoddard, J. (1994). Prevalence and impact of multiple childhood chronic illnesses. Journal of Pediatrics, 124(1), 40–48.CrossRefPubMed

70.

McDougall, J., King, G., DeWit, D., Offord, D., Meyer, K., & LaPorta, J. (2004). Chronic physical health conditions and disability among Canadian school-aged children: A national profile. Disability and Rehabilitation, 26(1), 35–45.CrossRefPubMed

71.

Colver, A. (2010). Why are children with cerebral palsy more likely to have emotional problems and behavioural difficulties? Developmental Medicine and Child Neurology, 52(11), 980–987.CrossRef

72.

Petrenchik, T., King, G., & Batorowicz, B. (2011). Children and youth with disabilities: Enhancing mental health through positive experiences of doing and belonging. In S. Bazyk (Ed.), Mental health promotion, prevention, and intervention in children and youth: A guiding framework for occupational therapy. Bethesda, MD: The American Occupational Therapy Association.

73.

Breau, L. (2011). A new reminder that pain hurts. Developmental Medicine and Child Neurology, 53(11), 971–978.CrossRef

74.

Baxter, P. (2011). Comorbidities of cerebral palsy need more emphasis—Especially pain. Developmental Medicine and Child Neurology, 55(5), 396.CrossRef

75.

Baldwin, P., McDougall, J., & Evans, J. (2008). An exploration of spirituality, spiritual beliefs and paediatric rehabilitation. Spiritual Heath International, 9(4), 249–262.CrossRef

76.

Baldwin, P., Evans, J., Nichols, M., Wright, V., & McDougall, J. (2015). Youth with disabilities talk about spirituality: A qualitative descriptive study. The Qualitative Report, 20(7), 941–958.

77.

King, G., Servais, M., Bolack, L., Shephard, T., & Willoughby, C. (2012). Development of a measure to assess listening and interactive communication skills in the delivery of children’s rehabilitation services. Disability and Rehabilitation, 34(6), 459–469.CrossRefPubMed

78.

Rosenbaum, P. (2011). Family and quality of life: Key elements in intervention in children with cerebral palsy. Developmental Medicine and Child Neurology, 53(Suppl. 4), 68–70.CrossRefPubMed

79.

McDougall, J., Evans, J., & Baldwin, P. (2010). The importance of self-determination to perceived quality of life for youth and young adults with chronic conditions and disabilities. Remedial and Special Education, 31(4), 252–260.CrossRef

80.

Lachapelle, Y., Wehmeyer, M., Haelewyck, M., Courbois, Y., Keith, K., Schalock, R., et al. (2005). The relationship between quality of life and self-determination: An international study. Journal of Intellectual Disability Research, 49(10), 740–744.CrossRefPubMed

Title: Three-year trajectories of global perceived quality of life for youth with chronic health conditions
Authors: Janette McDougall
David J. DeWit
Megan Nichols
Linda Miller
F. Virginia Wright
Publication date: 01-12-2016
Publisher: Springer International Publishing
Published in: Quality of Life Research / Issue 12/2016
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-016-1353-z

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Three-year trajectories of global perceived quality of life for youth with chronic health conditions

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 12/2016

The e-MSWS-12: improving the multiple sclerosis walking scale using item response theory

Asymmetric responsiveness of disability and health-related quality of life to improvement versus decline in Parkinson’s disease

Anthropometric measures change and quality of life in elderly people: a longitudinal population-based study in Southern Brazil

Health-related quality of life of among elders in rural China: the effect of widowhood

Psychometric assessment of the IBS-D Daily Symptom Diary and Symptom Event Log

Objective drivers of subjective well-being in geriatric inpatients: mobility function and level of education are general predictors of self-evaluated health, feeling of loneliness, and severity of depression symptoms