A novel data-driven model to define physical activity metrics and predict mortality in patients with chronic obstructive pulmonary disease.

Physical activity (PA) is a well-established prognostic marker in Chronic Obstructive Pulmonary Disease (COPD). Traditional PA metrics, such as step count, often overlook movement intensity, while energy expenditure (EE) relies on indirect calorimetry assumptions. To address these limitations, we propose a data-driven PA metric that integrates movement frequency and amplitude derived from raw accelerometer data. This retrospective analysis, based on a Dutch COPD database, also evaluates the predictive value of the new score for mortality in COPD patients compared to step count and energy expenditure. Movement data and step counts were collected using McRoberts triaxial accelerometers. Fourier analysis was applied to extract movement frequency and amplitude, which were then used to compute the physical activity (PA) score. Kolmogorov-Smirnov test was conducted to assess whether the distributions of step count and PA score differed, followed by Kruskal-Wallis test to assess day-to-day movement variability. Logistic regression was used to evaluate and compare the predictive performance of the novel PA score against step count and EE. A total of 404 COPD patients (51.5% female; median [IQR] age: 57 [46-66] years) were included. The proposed PA score and step count exhibited similar daily patterns but differed significantly in distribution. Mortality data were available for 165 participants. The PA score achieved 2.8% higher accuracy and 5.6% higher balanced accuracy than step count in mortality prediction, while EE demonstrated the lowest predictive performance. The proposed PA score demonstrates stronger predictive power for mortality in COPD patients, highlighting the importance of integrating movement characteristics beyond simple step count, and offering a more refined metric for PA evaluation in both clinical and research settings.