Abstract
INTRODUCTION: Interstitial lung disease (ILD) patients may develop a progressive phenotype usually characterised by progressive pulmonary fibrosis. While this condition is life-limiting, wide variations in its clinical course have made it difficult to predict the rate of disease progression, onset of acute exacerbations and mortality. New approaches are needed to predict the clinical course of ILD, to enable treatment planning, evaluation and clinical trial design. Advances in digital health technologies have facilitated the ability to collect 'real-time' data to monitor diseases. These data, including physiological measures, activity indices and patient-reported outcomes, may be useful as components of new outcome predictors. The objective of this study is to first deploy comprehensive data collection enabling deep profiling of patients with ILD and to use these data to develop better predictors of outcome. Finally, these predictions will be evaluated based on real observed outcomes for individual patients. METHODS AND ANALYSIS: This study is a prospective cohort study with 50 participants. INCLUSION CRITERIA: Age 18 years or older with a diagnosis of ILD and the ability to provide written informed consent. EXCLUSION CRITERIA: Age under 18 years or unwilling to wear a smartwatch for the duration of the study. Participants will be provided with a smartwatch to passively collect biometric data. These data will be combined with clinical history and course, in addition to a set of patient-reported outcome measures. Participants will be followed for 3 years to assess the rate of disease progression, occurrence of acute exacerbations and mortality. Initial data will be used to develop clinical prediction models. These models will be further evaluated for accuracy using regular follow-up data. ETHICS AND DISSEMINATION: This study was approved by the St. Vincent's University Hospital Research Ethics Committee, Dublin, Ireland (reference no: RS23-023). Results will be presented at medical conferences and disseminated via peer-reviewed journals.