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Ellen Ricke,1 Eric W Bakker2 1Department of Social Psychology, University of Groningen, Groningen, the Netherlands; 2Department of Epidemiology and Data Science | Division EBM, Academic Medical Centre, Amsterdam, the NetherlandsCorrespondence: Ellen Ricke, Department of Social Psychology, University of Groningen, Grote Kruisstraat 2/1, Groningen, 9712 TS, the Netherlands, Tel +31 6 211 817 02, Email E.Ricke@rug.nlPurpose: Pulmonary rehabilitation (PR) is considered a cost-effective method of improving health-related quality of life in patients with chronic obstructive pulmonary disease (COPD). However, increasing demand and increasing costs of supply demands for sustainable and affordable care. One of the possible solutions to keep care affordable is self-management. A challenge here is non-adherence. Understanding who are adherent and who are non-adherent could be helpful to differentiate between patients who need more or less support. Therefore, the aim of this study was to develop and validate a model to predict adherence to PR in patients with COPD.Patients and methods: A multivariable logistic regression model for exercise adherence was developed. Eight candidate predictors, that were prespecified, were obtained in a prospective cohort study from 196 patients with COPD following PR in 53 primary physiotherapy practices in the Netherlands and Belgium, between January 2021 and August 2022. To create a parsimonious model, variable selection using backward selection was performed with a p-value of > 0.05 for elimination. Model performance was assessed by discrimination, calibration and clinical utility. Internal validation was assessed by bootstrapping (n = 500).Results: The final model included four predictors: intention, depression, MRC-score and alliance. The optimism-corrected AUC after bootstrap internal validation was 0.79 (95% CI, 0.72– 0.85). Calibration plots suggested good calibration and decision curve analysis showed great net benefit in a wide range of risk thresholds.Conclusion: The exercise adherence prediction model has potential for clinical utility to predict adherence in patients with COPD. Information from such a model can be used to manage the patient instead of managing the disease, and thereby to determine the treatment frequency for each individual patient. As a result, healthcare capacity might be better distributed, potentially reducing pressure on healthcare without compromising the effectiveness of PR for the individual patient.Keywords: chronic obstructive pulmonary disease, sustainable healthcare, pulmonary rehabilitation, self-management