The PedsQL™ Infant Scales: feasibility, internal consistency reliability, and validity in healthy and ill infants
- 1.1k Downloads
The PedsQL™ (Pediatric Quality of Life Inventory™) is a modular instrument designed to measure health-related quality of life (HRQOL) and disease-specific symptoms in children and adolescents ages 2–18. The new PedsQL™ Infant Scales were designed as a generic HRQOL instrument specifically for healthy and ill infants ages 1–24 months. The objective of this study was to report on the initial feasibility, internal consistency reliability, and validity of the PedsQL™ Infant Scales in healthy, acutely ill, and chronically ill infants.
The 36-item (ages 1–12 months) and 45-item (ages 13–24 months) PedsQL™ Infant Scales (Physical Functioning, Physical Symptoms, Emotional Functioning, Social Functioning, Cognitive Functioning) were completed by 683 parents of healthy, acutely ill, and chronically ill infants.
The PedsQL™ Infant Scales evidenced minimal missing responses, achieved excellent internal consistency reliability for the Total Scale Scores (α = 0.92), distinguished between healthy infants and acutely and chronically ill infants, and demonstrated a confirmatory factor structure largely consistent with the a priori conceptual model.
The results demonstrate the initial measurement properties of the PedsQL™ Infant Scales in healthy and ill infants. The findings suggest that the PedsQL™ Infant Scales may be utilized in the evaluation of generic HRQOL in infants ages 1–24 months.
KeywordsPedsQL™ Infants Health-related quality of life Patient-reported outcomes
Pediatric Quality of Life Inventory™
Food and Drug Administration
Health-related quality of life
This research was funded in part by intramural grants from the Texas A&M University Research Foundation and the Scott and White Memorial Hospital Research Foundation.
Dr. Varni holds the copyright and the trademark for the PedsQL™ and receives financial compensation from the Mapi Research Trust, which is a non-profit research institute that charges distribution fees to for-profit companies that use the Pediatric Quality of Life Inventory™.
- 1.Varni, J. W., Burwinkle, T. M., & Lane, M. M. (2005). Health-related quality of life measurement in pediatric clinical practice: An appraisal and precept for future research and application. Health and Quality of Life Outcomes, 3(34), 1–9.Google Scholar
- 4.World Health Organization. (1948). Constitution of the World Health Organization: Basic document. Geneva, Switzerland: World Health Organization.Google Scholar
- 5.FDA. (2009). Guidance for Industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. Rockville, MD: Food and Drug Administration, U.S. Department of Health and Human Services.Google Scholar
- 8.Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). Parent proxy-report of their children’s health-related quality of life: An analysis of 13,878 parents’ reliability and validity across age subgroups using the PedsQL™ 4.0 Generic Core Scales. Health and Quality of Life Outcomes, 5(2), 1–10.CrossRefPubMedGoogle Scholar
- 17.Aday, L. A. (1996). Designing and conducting health surveys: A comprehensive guide (2nd ed.). San Francisco: Jossey-Bass.Google Scholar
- 18.Fowler, F. J. (1995). Improving survey questions: Design and evaluation. Thousand Oaks, CA: Sage.Google Scholar
- 19.Schwarz, N., & Sudman, N. (Eds.). (1996). Answering questions: Methodology for determining cognitive and communicative processes in survey research. San Francisco: Jossey-Bass.Google Scholar
- 20.Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials: Interdisciplinary statistics. New York: Chapman & Hall/CRC.Google Scholar
- 25.Nunnally, J. C., & Bernstein, I. R. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.Google Scholar
- 26.Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Erlbaum.Google Scholar
- 28.Fayers, P. M., & Machin, D. (2000). Quality of life: Assessment, analysis, and interpretation. New York: Wiley.Google Scholar
- 29.Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & Dickinson, P. (2002). 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, 94, 2090–2106.CrossRefPubMedGoogle Scholar
- 31.Varni, J. W., Seid, M., Knight, T. S., Burwinkle, T. M., Brown, J., & Szer, I. S. (2002). The PedsQL™ in pediatric rheumatology: Reliability, validity, and responsiveness of the Pediatric Quality of Life Inventory™ Generic Core Scales and Rheumatology Module. Arthritis and Rheumatism, 46, 714–725.CrossRefPubMedGoogle Scholar
- 32.Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
- 33.SPSS. (2008). SPSS 16.0 for Windows. Chicago: SPSS, Inc.Google Scholar
- 41.Hu. L., & Bentler, P. M. (1995). Evaluating model fit. In Hoyle, R. H. (Ed.) Structural equation modeling: Concepts, issues and applications (pp. 76–99). Thousand Oaks: Sage.Google Scholar
- 42.Joreskog, K. G., & Sorbom, D. (2003). LISREL 8.5. Lincolnwood, IL: Scientific Software International, Inc.Google Scholar