Chronic widespread pain remains a complex and incompletely understood condition. To complement existing pain assessment strategies, this study explored the ecological validity of unobtrusively captured daily life physiological signals as indicators of pain. Therefore, we collected physiological data using a wearable wristband from 46 patients with chronic widespread pain for seven days. Linear mixed-effect models revealed several significant associations between physiological signals, such as mean heart rate and momentary pain intensity. However, making individual pain predictions with multivariate machine learning models did not add value. While this study underscores the potential of ambulatory physiology for pain assessment, future research should validate and expand upon these initial findings to further enhance pain management strategies.
