Actigraphy informs distinct patient-centered outcomes in Pre-COPD.

Area of Science:

• Pulmonary Medicine
• Biomedical Engineering
• Wearable Technology

Background:

• Actigraphy offers patient-centered quantification of physical activity in real-world settings.
• Pre-COPD is characterized by air trapping but no spirometric obstruction.

Purpose of the Study:

• To characterize the relationship between actigraphy outputs and in-laboratory measures of cardiopulmonary function and respiratory symptoms in pre-COPD.
• To identify relevant actigraphy predictors using machine learning and examine their associations with symptoms, lung function, and exercise outcomes.

Main Methods:

• Actigraphy data collected for 8 hours/day for 5 consecutive days.
• In-laboratory assessment included respiratory questionnaires, pulmonary function tests (PFT), and cardiopulmonary exercise testing (CPET).
• Machine learning approaches (hierarchical cluster analysis, regression modeling) were used to analyze data from 61 pre-COPD subjects.

Main Results:

• Actigraphy activity, distance, and energy domains correlated with each other but were loosely associated with spirometric and peak exercise measures.
• The sedentary domain clustered with respiratory symptoms, air trapping, airflow indices, and ventilatory efficiency.
• Sedentary behavior was inversely associated with baseline lung volumes and peak exercise tidal breathing; activity domains were associated with anaerobic threshold (VO2AT).

Conclusions:

• Outpatient actigraphy provides real-world patient-centered outcomes beyond standardized respiratory assessments.
• Actigraphy's distinct activity and sedentary domains offer unique insights into pre-COPD patient status.