Faust, L

Abstract Rationale Despite its effectiveness at improving outcomes in patients with COPD, pulmonary rehabilitation (PR) attrition rates are high, often exceeding 50%. Fully remote programs, like home-based PR (HBPR), face additional challenges engaging patients in real-world settings. With the growing adoption of wearables, sensor-derived behavioral measures, like sleep patterns, may identify patients most likely to benefit from remote PR. This study evaluated whether incorporating baseline sleep measures from a wrist-worn activity monitor in machine learning (ML) models improved the prediction of 12-week engagement with HBPR in patients with COPD. Methods Among participants (n = 124), the following sleep measures were collected for 1 week before HBPR: sleep duration (min), standard deviation of sleep duration, wake-after-sleep-onset (WASO) (min), standard deviation of WASO, number of awakenings, length of awakenings (min), activity counts, movement index, movement fragmentation index, and sleep fragmentation index. Sleep measures were processed (1) using a previously validated Tudor-Locke algorithm and (2) applying partial least squares-discriminant analysis (PLS-DA) to generate a single latent variable (Composite Sleep Health Score) for inclusion in models. Engagement was defined as completion of one or more recommended activities per week for the full program duration (12 weeks). To determine if the inclusion of objective sleep measures improved the prediction of HBPR engagement, nested model comparisons for parametric (logistic regression and SVM) and non-parametric (decision tree and naïve bayes) ML models were performed. A sensitivity analysis (SA) replaced the Composite Sleep Health Score with sleep regularity (duration and WASO) measures only. Results In models adjusted for age, sex, Charlson Comorbidity Index, current smoker status, modified Medical Research Council (mMRC), and forced expiratory volume in 1 second (FEV1), the inclusion of the Composite Sleep Health Score significantly improved the prediction of 12-week engagement only in SVM models (AUC 0.755; p = 0.007). Specificity (18.2%) and accuracy (68.5%) also improved by 27.3% and 7.3%, respectively. The Composite Sleep Health Score and mMRC scores ranked most frequently as the variables of highest importance. Identical trends were observed in the SA. Conclusion Although preliminary, these findings support additional investigation into the use of wearable-derived sleep measures to improve screening for HBPR eligibility, identifying patients who will clinically benefit from fully remote PR. Building on these primary findings, future researchers should carefully select variables for model inclusion and initiate new hypotheses elucidating the link between objective sleep measures and HBPR outcomes in COPD patients. This abstract is funded by: NIH R56 HL173214, NIH R01 HL140486, and the Robert D. and Patricia E. Kern Center for the Science of Health Delivery