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Multiple 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.)

Abstract: Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.)