4.6 Article

Quality-of-Life Measures in Children With Neurological Conditions: Pediatric Neuro-QOL

Journal

NEUROREHABILITATION AND NEURAL REPAIR
Volume 26, Issue 1, Pages 36-47

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/1545968311412054

Keywords

health-related quality of life; children; neurological disorders; item bank; item response theory

Funding

  1. National Institute of Neurological Disorders and Stroke [HHSN265200423601C]
  2. NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [N01NS042360] Funding Source: NIH RePORTER

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Background. A comprehensive, reliable, and valid measurement system is needed to monitor changes in children with neurological conditions who experience lifelong functional limitations. Objective. This article describes the development and psychometric properties of the pediatric version of the Quality of Life in Neurological Disorders (Neuro-QOL) measurement system. Methods. The pediatric Neuro-QOL consists of generic and targeted measures. Literature review, focus groups, individual interviews, cognitive interviews of children and consensus meetings were used to identify and finalize relevant domains and item content. Testing was conducted on 1018 children aged 10 to 17 years drawn from the US general population for generic measures and 171 similarly aged children with muscular dystrophy or epilepsy for targeted measures. Dimensionality was evaluated using factor analytic methods. For unidimensional domains, item parameters were estimated using item response theory models. Measures with acceptable fit indices were calibrated as item banks; those without acceptable fit indices were treated as summary scales. Results. Ten measures were developed: 8 generic or targeted banks (anxiety, depression, anger, interaction with peers, fatigue, pain, applied cognition, and stigma) and 2 generic scales (upper and lower extremity function). The banks reliably (r > 0.90) measured 63.2% to 100% of the children tested. Conclusions. The pediatric Neuro-QOL is a comprehensive measurement system with acceptable psychometric properties that could be used in computerized adaptive testing. The next step is to validate these measures in various clinical populations.

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