Data-Driven Assessment of Lung Cancer Patients Using Performance Status and Wearable Device Metrics.

Abstract

Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for 85% of cases. Traditional methods for assessing the clinical status of cancer patients, such as Performance Status (PS), are subjective and may lack consistency across clinicians. Lung cancer remains a leading cause of cancer-related mortality worldwide. Monitoring the physical activity and PS of patients undergoing treatment is crucial for tailored therapeutic interventions. The LUPA study is a non-interventional, two-phase observational study aimed at assessing the usability of wearable devices and a mobile application for monitoring activity, sleep quality, and symptoms in lung cancer patients. A mixed-methods approach was used in Phase I to assess usability and data utility, while Phase II involved a one-group observational clinical study with 61 patients to explore correlations between clinician-reported PS and data collected through wearables. The results suggest moderate correlations between wearable data and ECOG-PS scores, but challenges remain in applying machine learning (ML) models to predict changes in patient condition. Future work should address model refinement, increased sample size, and the incorporation of additional features from wearable devices to enhance predictive accuracy.