Innovative pain outcomes derived from patients real-time pain reports

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 DESCRIPTION (provided by applicant): Self-reports of pain intensity are indispensable for understanding patients' health and for evaluating core outcomes in clinical trials. Recent methodological advances using real-time pain measurement (Ecological Momentary Assessment) have made it possible to capture patients' pain experiences with high resolution. This has the potential to improve outcomes for behavioral and medical research by considering temporal aspects and environmental correlates of pain intensity. However, to date, outcome measures created from momentary data have generally been limited to the average level of pain intensity. In the proposed research we will examine the utility and validity of an expanded set of pain indices derived from patients' momentary pain intensity reports and will determine which are most relevant to stakeholders. There are two broad stages in the proposed research plan. First, we will capture stakeholders' (patients, healthcare providers, regulators, clinical trialist) impressions of the momentary pain patterns that are most important to them; this will form the basis for creating additional pain indices that could improve understanding of treatment effects and provide richer insight into clinical outcomes. Second, the psychometric properties of the new indices will be thoroughly examined in extensive secondary data analyses of many existing momentary pain datasets from academic studies and pharmaceutical trials. These analyses will provide a comprehensive evaluation of the reliability, validity, and sensitivity of the indices, as well as their relationships with other key outcomes in pain clinical research. Results will be synthesized across studies using meta-analytic principles to evaluate their robustness and generalizability across measurement and study characteristics. Novel indices of pain status derived from real-time assessments will refine pain measurement for researchers trying to understand the determinants of pain and developing interventions for pain. Such information has the potential to (1) advance knowledge about etiology, (2) facilitate diagnostic classification of pain conditions, (3) provide new information about patients' pain that could be used to tailor treatments, and (4) augment understanding of the effects of pain interventions.