Methods: PA was monitored in 60 patients suffering from chronic intractable pain and in 20 pain-free healthy subjects. The measurements were performed under free-living conditions during five consecutive days using body-fixed inertial sensors. The PA patterns were modeled, analyzed and quantified in the context of two different mathematical approaches:
(1) the theory of stochastic bivariate point processes by modeling the PA pattern as a sequence of events, i.e. the sequence of activity-to-rest and rest-to-activity transitions;
(2) the concept of dynamical structural complexity by mapping the various features of PA, i.e. type, intensity and duration into a ‘barcode’. The complexity of PA barcode was quantified using the Lempel-Zip complexity end entropic measures.
Results:The derived PA metrics were compared between age-matched groups of subjects with clinically different pain intensities. The statistical analysis indicated significant differences between groups (p<0.05). As hypothesized, the dynamics of PA patterns revealed clinically relevant information and provided empirical evidence for complex pain behavioral models classified as fear-avoidance, endurance and pacing.
Conclusions: Ambulatory monitoring devices and advanced mathematical methods provide the opportunity to objectively quantify a number of PA features including dynamical structural complexity of long-term recorded activity patterns. The derived parameters are able to distinguish chronic pain conditions and may have other potential clinical applications in neurological diseases (depression, Parkinson, Alzheimer), geriatrics, cardio-vascular diseases.