Background Caring for a child with cystic fibrosis (CF) is a significant daily commitment for parent/caregivers. A tool that quantifies the challenge and the impact on the parent/caregivers would be useful, both clinically to monitor treatment burden and support families, and also as an important pragmatic outcome measure for clinical trials. The Challenge of Living with Cystic Fibrosis (CLCF) questionnaire comprehensively assesses the impact on families of caring for a child with cystic fibrosis (CF). However its length (249 items) was reported to be overly taxing on respondents. The aims of this study were: 1) To create a streamlined CLCF, the CLCF-Short Form (CLCF-SF), 2) To demonstrate the reliability of the CLCF-SF, 3) To establish the validity of the measure.
Methods The CLCF was administered to caregivers with at least one child aged ≤14 years with CF. Data was pooled from three UK cohorts. In addition, caregivers completed the Beck Depression Inventory (BDI), State Trait Inventory (STAI) and the Revised Cystic Fibrosis Questionnaire (CFQ-R). A genetic algorithm, coded in R, was applied to a subset of items in order to select the optimal CLCF items which best reflected the overall construct. A genetic algorithm is an iterative technique used for the optimisation of problems which operates via evolution of increasingly apt solutions to those problems i.e. the selection of a set of optimal items according to some criteria. The reliability of the reduced scale was assessed using CTT and IRT methods.
Results In total, 135 caregivers completed the CLCF. Fifteen items were selected using the algorithm. The CLCF-SF had good internal reliability with a Cronbach's alpha of 0.82 (95% CI 0.77, 0.88). Convergent validity of the measure was demonstrated through correlations with BDI (0.48), STAI State measure (0.41), STAI Trait measure (0.43), summed CFQ-R treatment score (0.18), and model-based scores of caregiver treatment management (0.49) and child treatment management (0.46).
Conclusion The CLCF-SF retains the essence of the CLCF, has excellent internal consistency (reliability) and, given that the CLCF-SF score increases with intensified psychological and treatment demands, evidence for its validity on the sample in question is strong too. The CLCF-SF promises to be a robust clinical measure, and as a pragmatic outcome measure for intervention studies to improve care for children with CF. Use of a genetic algorithm for optimal item selection, according to a set of criteria, has the potential to change the way outcome measures are constructed.