• Diego Vitali, Charlotte S C Woolley, Amanda Ly, Matthew Nunes, Laura Oporto Lisboa, Edmund Keogh, John McBeth, Beate Ehrhardt, Amanda C de C Williams, and Christopher Eccleston.
• Research Department of Clinical, Educational & Health Psychology, University College London, London, UK. Electronic address: d.vitali@ucl.ac.uk.
• J Pain. 2024 Sep 17; 26: 104679104679.

AbstractMultiple large longitudinal cohorts provide opportunities to address questions about predictors of pain and pain trajectories, even when not anticipated in the design of the historical databases. This focus article uses 2 empirical examples to illustrate the processes of assessing the measurement properties of data from large cohort studies to answer questions about pain. In both examples, data were screened to select candidate variables that captured the impact of chronic pain on self-care activities, productivity, and social activities. We describe a series of steps to select candidate items and evaluate their psychometric characteristics in relation to the measurement of pain impact proposed. In the UK Biobank, a general lack of internal consistency of variables selected prevented the identification of a satisfactory measurement model, with lessons for the measurement of chronic pain impact. In the English Longitudinal Study of Ageing, a measurement model for chronic pain impact was identified, albeit limited to capturing the impact of pain on self-care and productivity but lacking coverage related to social participation. In conjunction with its Supplementary Material, this focus article aims to encourage exploration of these valuable prospectively collected data to support researchers to make explicit the relationships between items in the databases and constructs of interest in pain research and to use empirical methods to estimate the possible biases in these variables. PERSPECTIVE: This focus article outlines a theory-driven approach for fitting new measurement models to data from large cohort studies and evaluating their psychometric properties. This aims to help researchers develop an empirical understanding of the gains and limitations connected with the process of repurposing the data stored in these datasets.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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