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Gepubliceerd in:

20-06-2023

Health-related quality of life profiles in adolescents and young adults with chronic conditions

Auteurs: Suwei Wang, Cara J. Arizmendi, Dan V. Blalock, Dandan Chen, Li Lin, David Thissen, I-Chan Huang, Darren A. DeWalt, Bryce B. Reeve

Gepubliceerd in: Quality of Life Research | Uitgave 11/2023

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Abstract

Purpose

To assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions.

Methods

AYAs (N = 872) aged 14–20 years completed NIH’s Patient-Reported Outcomes Measurement Information System® (PROMIS®) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores. The optimal number of profiles was determined by model fit statistics, likelihood ratio test, and entropy. Multinomial logistic regression models were used to examine how LPA’s HRQOL profile membership was associated with patient demographic and chronic conditions. The model prediction accuracy on profile membership was evaluated using Huberty’s I index with a threshold of 0.35 for good effect.

Results

A 4-profile LPA model was selected. A total of 161 (18.5%), 256 (29.4%), 364 (41.7%), and 91 (10.4%) AYAs were classified into Minimal, Mild, Moderate, and Severe HRQOL Impact profiles. AYAs in each profile had distinctive mean scores with over a half standard deviation (5-points in PROMIS T-scores) of difference between profiles across most HRQOL domains. AYAs who were female or had conditions such as mental health condition, hypertension, and self-reported chronic pain were more likely to be in the Severe HRQOL Impact profile. The Huberty’s I index was 0.36.

Conclusions

Approximately half of AYAs with a chronic condition experience moderate to severe HRQOL impact. The availability of risk prediction models for HRQOL impact will help to identify AYAs who are in greatest need of closer clinical care follow-up.
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Literatuur
1.
go back to reference Gore, F. M., et al. (2011). Global burden of disease in young people aged 10–24 years: A systematic analysis. The Lancet, 377(9783), 2093–2102.CrossRef Gore, F. M., et al. (2011). Global burden of disease in young people aged 10–24 years: A systematic analysis. The Lancet, 377(9783), 2093–2102.CrossRef
3.
go back to reference Sprangers, M. A. G. (2002). Quality-of-life assessment in oncology. Acta Oncologica, 41(3), 229–237.PubMedCrossRef Sprangers, M. A. G. (2002). Quality-of-life assessment in oncology. Acta Oncologica, 41(3), 229–237.PubMedCrossRef
4.
go back to reference Cella, D. F., & Tulsky, D. S. (1990). Measuring quality of life today: methodological aspects. Oncology (Williston Park, N.Y.), 4(5), 29–38. discussion 69.PubMed Cella, D. F., & Tulsky, D. S. (1990). Measuring quality of life today: methodological aspects. Oncology (Williston Park, N.Y.), 4(5), 29–38. discussion 69.PubMed
5.
go back to reference Siegrist, J., & Junge, A. (1989). Conceptual and methodological problems in research on the quality of life in clinical medicine. Social Science and Medicine, 29(3), 463–468.PubMedCrossRef Siegrist, J., & Junge, A. (1989). Conceptual and methodological problems in research on the quality of life in clinical medicine. Social Science and Medicine, 29(3), 463–468.PubMedCrossRef
6.
go back to reference Stewart, A. L., et al. (1989). Functional status and well-being of patients with chronic conditions: Results from the medical outcomes study. JAMA, 262(7), 907–913.PubMedCrossRef Stewart, A. L., et al. (1989). Functional status and well-being of patients with chronic conditions: Results from the medical outcomes study. JAMA, 262(7), 907–913.PubMedCrossRef
7.
go back to reference Schlenk, E. A., et al. (1997). Health-related quality of life in chronic disorders: A comparison across studies using the MOS SF-36. Quality of life research, 7(1), 57–65.CrossRef Schlenk, E. A., et al. (1997). Health-related quality of life in chronic disorders: A comparison across studies using the MOS SF-36. Quality of life research, 7(1), 57–65.CrossRef
8.
go back to reference Langeveld, N. E., et al. (2002). Quality of life in young adult survivors of childhood cancer. Supportive Care in Cancer, 10(8), 579–600.PubMedCrossRef Langeveld, N. E., et al. (2002). Quality of life in young adult survivors of childhood cancer. Supportive Care in Cancer, 10(8), 579–600.PubMedCrossRef
9.
go back to reference Ashing-Giwa, K. T., et al. (2007). Examining predictive models of HRQOL in a population-based, multiethnic sample of women with breast carcinoma. Quality of Life Research, 16(3), 413–428.PubMedCrossRef Ashing-Giwa, K. T., et al. (2007). Examining predictive models of HRQOL in a population-based, multiethnic sample of women with breast carcinoma. Quality of Life Research, 16(3), 413–428.PubMedCrossRef
10.
go back to reference Bellizzi, K. M., et al. (2012). Double jeopardy? Age, race, and HRQOL in older adults with cancer. Journal of Cancer Epidemiology, 2012, 1–9.CrossRef Bellizzi, K. M., et al. (2012). Double jeopardy? Age, race, and HRQOL in older adults with cancer. Journal of Cancer Epidemiology, 2012, 1–9.CrossRef
11.
go back to reference Clauser, S. B., et al. (2008). Disparities in HRQOL of cancer survivors and non-cancer managed care enrollees. Health Care Financing Review, 29(4), 23.PubMedPubMedCentral Clauser, S. B., et al. (2008). Disparities in HRQOL of cancer survivors and non-cancer managed care enrollees. Health Care Financing Review, 29(4), 23.PubMedPubMedCentral
12.
go back to reference Deimling, G. T., et al. (2005). The health of older-adult, long-term cancer survivors. Cancer Nursing, 28(6), 415–424.PubMedCrossRef Deimling, G. T., et al. (2005). The health of older-adult, long-term cancer survivors. Cancer Nursing, 28(6), 415–424.PubMedCrossRef
13.
go back to reference Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). 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, 5(1), 43.PubMedPubMedCentralCrossRef Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). 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, 5(1), 43.PubMedPubMedCentralCrossRef
14.
go back to reference Uzark, K., et al. (2008). Quality of life in children with heart disease as perceived by children and parents. Pediatrics, 121(5), e1060–e1067.PubMedCrossRef Uzark, K., et al. (2008). Quality of life in children with heart disease as perceived by children and parents. Pediatrics, 121(5), e1060–e1067.PubMedCrossRef
15.
go back to reference Devinsky, O., et al. (1999). Risk factors for poor health-related quality of life in adolescents with epilepsy. Epilepsia, 40(12), 1715–1720.PubMedCrossRef Devinsky, O., et al. (1999). Risk factors for poor health-related quality of life in adolescents with epilepsy. Epilepsia, 40(12), 1715–1720.PubMedCrossRef
17.
go back to reference Gaspar, T., et al. (2010). Quality of life: differences related to gender, age, socio-economic status and health status, in Portuguese teens. Revista de Psicologia da Criança e do Adolescente, 2, 87–104. Gaspar, T., et al. (2010). Quality of life: differences related to gender, age, socio-economic status and health status, in Portuguese teens. Revista de Psicologia da Criança e do Adolescente, 2, 87–104.
18.
go back to reference Colver, A., et al. (2015). Self-reported quality of life of adolescents with cerebral palsy: A cross-sectional and longitudinal analysis. The Lancet, 385(9969), 705–716.CrossRef Colver, A., et al. (2015). Self-reported quality of life of adolescents with cerebral palsy: A cross-sectional and longitudinal analysis. The Lancet, 385(9969), 705–716.CrossRef
19.
go back to reference Parkinson, K. N., et al. (2015). Influence of adiposity on health-related quality of life in the Gateshead Millennium Study cohort: Longitudinal study at 12 years. Archives of Disease in Childhood, 100(8), 779–783.PubMedCrossRef Parkinson, K. N., et al. (2015). Influence of adiposity on health-related quality of life in the Gateshead Millennium Study cohort: Longitudinal study at 12 years. Archives of Disease in Childhood, 100(8), 779–783.PubMedCrossRef
20.
go back to reference Kaczmarek, C., Haller, D. M., & Yaron, M. (2016). Health-related quality of life in adolescents and young adults with polycystic ovary syndrome: A systematic review. Journal of pediatric and adolescent gynecology, 29(6), 551–557.PubMedCrossRef Kaczmarek, C., Haller, D. M., & Yaron, M. (2016). Health-related quality of life in adolescents and young adults with polycystic ovary syndrome: A systematic review. Journal of pediatric and adolescent gynecology, 29(6), 551–557.PubMedCrossRef
22.
go back to reference Jennes-Coussens, M., Magill-Evans, J., & Koning, C. (2006). The quality of life of young men with Asperger syndrome: A brief report. Autism, 10(4), 403–414.PubMedCrossRef Jennes-Coussens, M., Magill-Evans, J., & Koning, C. (2006). The quality of life of young men with Asperger syndrome: A brief report. Autism, 10(4), 403–414.PubMedCrossRef
23.
go back to reference Jörngården, A., Wettergen, L., & von Essen, L. (2006). Measuring health-related quality of life in adolescents and young adults: Swedish normative data for the SF-36 and the HADS, and the influence of age, gender, and method of administration. Health and Quality of Life Outcomes, 4(1), 1–10.CrossRef Jörngården, A., Wettergen, L., & von Essen, L. (2006). Measuring health-related quality of life in adolescents and young adults: Swedish normative data for the SF-36 and the HADS, and the influence of age, gender, and method of administration. Health and Quality of Life Outcomes, 4(1), 1–10.CrossRef
24.
go back to reference Smith, A. W., et al. (2013). Health-related quality of life of adolescent and young adult patients with cancer in the United States: The adolescent and young adult health outcomes and patient experience study. Journal of Clinical Oncology, 31(17), 2136–2145.PubMedPubMedCentralCrossRef Smith, A. W., et al. (2013). Health-related quality of life of adolescent and young adult patients with cancer in the United States: The adolescent and young adult health outcomes and patient experience study. Journal of Clinical Oncology, 31(17), 2136–2145.PubMedPubMedCentralCrossRef
25.
go back to reference Nelson, T. D., et al. (2014). Health-related quality of life among adolescents in residential care: Description and correlates. American Journal of Orthopsychiatry, 84(3), 226.PubMedCrossRef Nelson, T. D., et al. (2014). Health-related quality of life among adolescents in residential care: Description and correlates. American Journal of Orthopsychiatry, 84(3), 226.PubMedCrossRef
26.
go back to reference Pemberger, S., et al. (2005). Quality of life in long-term childhood cancer survivors and the relation of late effects and subjective well-being. Supportive Care in Cancer, 13(1), 49–56.PubMedCrossRef Pemberger, S., et al. (2005). Quality of life in long-term childhood cancer survivors and the relation of late effects and subjective well-being. Supportive Care in Cancer, 13(1), 49–56.PubMedCrossRef
27.
go back to reference Wang, J., & Lanza, S. T. (2010). Preface of methods and applications of mixture models, special journal issue of advances and applications of statistical sciences. Advances and Applications in Statistical Sciences, 3, 1–6. Wang, J., & Lanza, S. T. (2010). Preface of methods and applications of mixture models, special journal issue of advances and applications of statistical sciences. Advances and Applications in Statistical Sciences, 3, 1–6.
28.
go back to reference Wang, J., & Wang, X. (2012). Structural equation modeling with Mplus: Methods and applications. Wiley.CrossRef Wang, J., & Wang, X. (2012). Structural equation modeling with Mplus: Methods and applications. Wiley.CrossRef
29.
go back to reference Buckner, T. W., et al. (2014). Patterns of symptoms and functional impairments in children with cancer. Pediatric Blood & Cancer, 61(7), 1282–1288.CrossRef Buckner, T. W., et al. (2014). Patterns of symptoms and functional impairments in children with cancer. Pediatric Blood & Cancer, 61(7), 1282–1288.CrossRef
30.
go back to reference Hinds, P. S., et al. (2021). Subjective toxicity profiles of children in treatment for cancer: A new guide to supportive care? Journal of Pain and Symptom Management, 61(6), 1188-1195.e2.PubMedCrossRef Hinds, P. S., et al. (2021). Subjective toxicity profiles of children in treatment for cancer: A new guide to supportive care? Journal of Pain and Symptom Management, 61(6), 1188-1195.e2.PubMedCrossRef
31.
go back to reference Barsevick, A. M., & Aktas, A. (2013). Cancer symptom cluster research: New perspectives and tools. Current Opinion in Supportive and Palliative Care, 7(1), 36–37.PubMedCrossRef Barsevick, A. M., & Aktas, A. (2013). Cancer symptom cluster research: New perspectives and tools. Current Opinion in Supportive and Palliative Care, 7(1), 36–37.PubMedCrossRef
32.
go back to reference Davis, P. J., et al. (2003). Multidimensional subgroups in migraine: Differential treatment outcome to a pain medicine program. Pain Medicine, 4(3), 215–222.PubMedCrossRef Davis, P. J., et al. (2003). Multidimensional subgroups in migraine: Differential treatment outcome to a pain medicine program. Pain Medicine, 4(3), 215–222.PubMedCrossRef
33.
go back to reference Miaskowski, C., et al. (2006). Online exclusive-subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: A cluster analysis. Oncology Nursing Forum, 33, E79–E89.PubMedCrossRef Miaskowski, C., et al. (2006). Online exclusive-subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: A cluster analysis. Oncology Nursing Forum, 33, E79–E89.PubMedCrossRef
34.
go back to reference Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly (1982-), 52, 377–389.CrossRef Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly (1982-), 52, 377–389.CrossRef
35.
go back to reference Stewart, D. W. (1981). The application and misapplication of factor analysis in marketing research. Journal of Marketing Research, 18(1), 51–62.CrossRef Stewart, D. W. (1981). The application and misapplication of factor analysis in marketing research. Journal of Marketing Research, 18(1), 51–62.CrossRef
36.
go back to reference Olaya, B., et al. (2017). Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: A prospective cohort study. BMC Geriatrics, 17, 1–10.CrossRef Olaya, B., et al. (2017). Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: A prospective cohort study. BMC Geriatrics, 17, 1–10.CrossRef
37.
go back to reference Larsen, F. B., et al. (2017). A latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of Danish adults. PLoS ONE, 12(1), e0169426.PubMedPubMedCentralCrossRef Larsen, F. B., et al. (2017). A latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of Danish adults. PLoS ONE, 12(1), e0169426.PubMedPubMedCentralCrossRef
38.
go back to reference Garey, L., et al. (2019). Health-related quality of life among homeless smokers: Risk and protective factors of latent class membership. Behavioral Medicine, 45(1), 40–51.PubMedCrossRef Garey, L., et al. (2019). Health-related quality of life among homeless smokers: Risk and protective factors of latent class membership. Behavioral Medicine, 45(1), 40–51.PubMedCrossRef
40.
go back to reference Kenzik, K. M., et al. (2015). Health-related quality of life in lung cancer survivors: Latent class and latent transition analysis. Cancer, 121(9), 1520–1528.PubMedCrossRef Kenzik, K. M., et al. (2015). Health-related quality of life in lung cancer survivors: Latent class and latent transition analysis. Cancer, 121(9), 1520–1528.PubMedCrossRef
42.
go back to reference Grant, R. W., et al. (2020). Use of latent class analysis and k-means clustering to identify complex patient profiles. JAMA Network Open, 3(12), e2029068.PubMedPubMedCentralCrossRef Grant, R. W., et al. (2020). Use of latent class analysis and k-means clustering to identify complex patient profiles. JAMA Network Open, 3(12), e2029068.PubMedPubMedCentralCrossRef
43.
go back to reference Băjenaru, L., et al. (2022). Latent profile analysis for quality of life in older patients. BMC Geriatrics, 22(1), 1–7.CrossRef Băjenaru, L., et al. (2022). Latent profile analysis for quality of life in older patients. BMC Geriatrics, 22(1), 1–7.CrossRef
44.
go back to reference Michie, S., Miles, J., & Weinman, J. (2003). Patient-centredness in chronic illness: What is it and does it matter? Patient Education and Counseling, 51(3), 197–206.PubMedCrossRef Michie, S., Miles, J., & Weinman, J. (2003). Patient-centredness in chronic illness: What is it and does it matter? Patient Education and Counseling, 51(3), 197–206.PubMedCrossRef
45.
go back to reference Wong, A. W., et al. (2023). Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study. PLoS ONE, 18(6), e0286588.PubMedPubMedCentralCrossRef Wong, A. W., et al. (2023). Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study. PLoS ONE, 18(6), e0286588.PubMedPubMedCentralCrossRef
46.
go back to reference Shoop-Worrall, S. J., et al. (2021). Patient-reported wellbeing and clinical disease measures over time captured by multivariate trajectories of disease activity in individuals with juvenile idiopathic arthritis in the UK: A multicentre prospective longitudinal study. The Lancet Rheumatology, 3(2), e111–e121.PubMedCrossRef Shoop-Worrall, S. J., et al. (2021). Patient-reported wellbeing and clinical disease measures over time captured by multivariate trajectories of disease activity in individuals with juvenile idiopathic arthritis in the UK: A multicentre prospective longitudinal study. The Lancet Rheumatology, 3(2), e111–e121.PubMedCrossRef
47.
go back to reference Basch, E., et al. (2022). Effect of electronic symptom monitoring on patient-reported outcomes among patients with metastatic cancer: A randomized clinical trial. JAMA, 327(24), 2413–2422.PubMedPubMedCentralCrossRef Basch, E., et al. (2022). Effect of electronic symptom monitoring on patient-reported outcomes among patients with metastatic cancer: A randomized clinical trial. JAMA, 327(24), 2413–2422.PubMedPubMedCentralCrossRef
48.
go back to reference Lee, J. J., et al. (2021). Longitudinal analysis of symptom-based clustering in patients with primary Sjogren’s syndrome: A prospective cohort study with a 5-year follow-up period. Journal of Translational Medicine, 19, 1–8.CrossRef Lee, J. J., et al. (2021). Longitudinal analysis of symptom-based clustering in patients with primary Sjogren’s syndrome: A prospective cohort study with a 5-year follow-up period. Journal of Translational Medicine, 19, 1–8.CrossRef
50.
go back to reference Kiresuk, T. J., Smith, A., & Cardillo, J. E. (2014). Goal attainment scaling: Applications, theory, and measurement. Psychology Press.CrossRef Kiresuk, T. J., Smith, A., & Cardillo, J. E. (2014). Goal attainment scaling: Applications, theory, and measurement. Psychology Press.CrossRef
51.
go back to reference Cairns, A., et al. (2015). Setting measurable goals with young people: Qualitative feedback from the Goal Attainment Scale in youth mental health. British Journal of Occupational Therapy, 78(4), 253–259.CrossRef Cairns, A., et al. (2015). Setting measurable goals with young people: Qualitative feedback from the Goal Attainment Scale in youth mental health. British Journal of Occupational Therapy, 78(4), 253–259.CrossRef
52.
go back to reference Reeve, B. B., et al. (2016). Linkage between the PROMIS® pediatric and adult emotional distress measures. Quality of Life Research, 25(4), 823–833.PubMedCrossRef Reeve, B. B., et al. (2016). Linkage between the PROMIS® pediatric and adult emotional distress measures. Quality of Life Research, 25(4), 823–833.PubMedCrossRef
53.
go back to reference Neff, J. M., et al. (2002). Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system. Ambulatory Pediatrics, 2(1), 71–79.PubMedCrossRef Neff, J. M., et al. (2002). Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system. Ambulatory Pediatrics, 2(1), 71–79.PubMedCrossRef
54.
go back to reference Bethell, C. D., et al. (2002). Identifying children with special health care needs: Development and evaluation of a short screening instrument. Ambulatory Pediatrics, 2(1), 38–48.PubMedCrossRef Bethell, C. D., et al. (2002). Identifying children with special health care needs: Development and evaluation of a short screening instrument. Ambulatory Pediatrics, 2(1), 38–48.PubMedCrossRef
55.
57.
go back to reference Blalock, D. V., et al. (2020). Analysis of differential item functioning in PROMIS® pediatric and adult measures between adolescents and young adults with special health care needs. Psychological Test and Assessment Modeling, 62(4), 417–428. Blalock, D. V., et al. (2020). Analysis of differential item functioning in PROMIS® pediatric and adult measures between adolescents and young adults with special health care needs. Psychological Test and Assessment Modeling, 62(4), 417–428.
58.
go back to reference Bakk, Z., & Kuha, J. (2021). Relating latent class membership to external variables: An overview. British Journal of Mathematical and Statistical Psychology, 74(2), 340–362.PubMedCrossRef Bakk, Z., & Kuha, J. (2021). Relating latent class membership to external variables: An overview. British Journal of Mathematical and Statistical Psychology, 74(2), 340–362.PubMedCrossRef
60.
go back to reference Spurk, D., et al. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445.CrossRef Spurk, D., et al. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445.CrossRef
61.
go back to reference Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. Modern statistical methods for HCI (pp. 275–287). Springer.CrossRef Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. Modern statistical methods for HCI (pp. 275–287). Springer.CrossRef
62.
go back to reference Lo, Y. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.CrossRef Lo, Y. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.CrossRef
63.
go back to reference Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569.CrossRef Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569.CrossRef
64.
go back to reference Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195–212.CrossRef Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195–212.CrossRef
65.
go back to reference Huberty, C. J., & Lowman, L. L. (2000). Group overlap as a basis for effect size. Educational and Psychological Measurement, 60(4), 543–563.CrossRef Huberty, C. J., & Lowman, L. L. (2000). Group overlap as a basis for effect size. Educational and Psychological Measurement, 60(4), 543–563.CrossRef
66.
go back to reference Granado, E. A. (2015). Comparing three effect sizes for latent class analysis. University of North Texas Doctoral Dissertation. Granado, E. A. (2015). Comparing three effect sizes for latent class analysis. University of North Texas Doctoral Dissertation.
67.
go back to reference Bakk, Z., & Kuha, J. (2018). Two-step estimation of models between latent classes and external variables. Psychometrika, 83(4), 871–892.PubMedCrossRef Bakk, Z., & Kuha, J. (2018). Two-step estimation of models between latent classes and external variables. Psychometrika, 83(4), 871–892.PubMedCrossRef
68.
go back to reference Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.CrossRef Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.CrossRef
69.
go back to reference Terwee, C. B., et al. (2021). Minimal important change (MIC): A conceptual clarification and systematic review of MIC estimates of PROMIS measures. Quality of Life Research, 30(10), 2729–2754.PubMedPubMedCentralCrossRef Terwee, C. B., et al. (2021). Minimal important change (MIC): A conceptual clarification and systematic review of MIC estimates of PROMIS measures. Quality of Life Research, 30(10), 2729–2754.PubMedPubMedCentralCrossRef
70.
go back to reference Chalkiadis, G. A. (2001). Management of chronic pain in children. Medical Journal of Australia, 175(9), 476–479.PubMedCrossRef Chalkiadis, G. A. (2001). Management of chronic pain in children. Medical Journal of Australia, 175(9), 476–479.PubMedCrossRef
71.
go back to reference Roth-Isigkeit, A., et al. (2005). Pain among children and adolescents: Restrictions in daily living and triggering factors. Pediatrics, 115(2), e152–e162.PubMedCrossRef Roth-Isigkeit, A., et al. (2005). Pain among children and adolescents: Restrictions in daily living and triggering factors. Pediatrics, 115(2), e152–e162.PubMedCrossRef
73.
go back to reference Hunfeld, J. A. M. (2001). Chronic pain and its impact on quality of life in adolescents and their families. Journal of Pediatric Psychology, 26(3), 145–153.PubMedCrossRef Hunfeld, J. A. M. (2001). Chronic pain and its impact on quality of life in adolescents and their families. Journal of Pediatric Psychology, 26(3), 145–153.PubMedCrossRef
74.
go back to reference Gold, J. I., et al. (2009). Pediatric chronic pain and health-related quality of life. Journal of Pediatric Nursing, 24(2), 141–150.PubMedCrossRef Gold, J. I., et al. (2009). Pediatric chronic pain and health-related quality of life. Journal of Pediatric Nursing, 24(2), 141–150.PubMedCrossRef
75.
go back to reference Quittner, A. L., et al. (2010). Impact of socioeconomic status, race, and ethnicity on quality of life in patients with cystic fibrosis in the United States. Chest, 137(3), 642–650.PubMedCrossRef Quittner, A. L., et al. (2010). Impact of socioeconomic status, race, and ethnicity on quality of life in patients with cystic fibrosis in the United States. Chest, 137(3), 642–650.PubMedCrossRef
76.
go back to reference Tulsky, D. S., et al. (2019). Determining a transitional scoring link between PROMIS® pediatric and adult physical health measures. Quality of Life Research, 28(5), 1217–1229.PubMedCrossRef Tulsky, D. S., et al. (2019). Determining a transitional scoring link between PROMIS® pediatric and adult physical health measures. Quality of Life Research, 28(5), 1217–1229.PubMedCrossRef
Metagegevens
Titel
Health-related quality of life profiles in adolescents and young adults with chronic conditions
Auteurs
Suwei Wang
Cara J. Arizmendi
Dan V. Blalock
Dandan Chen
Li Lin
David Thissen
I-Chan Huang
Darren A. DeWalt
Bryce B. Reeve
Publicatiedatum
20-06-2023
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 11/2023
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-023-03463-5