title: Barcoding human physical activity to assess chronic pain conditions.
abstract: BACKGROUND
  Modern theories define chronic pain as a multidimensional experience - the result of complex interplay between physiological and psychological factors with significant impact on patients' physical, emotional and social functioning. The development of reliable assessment tools capable of capturing the multidimensional impact of chronic pain has challenged the medical community for decades. A number of validated tools are currently used in clinical practice however they all rely on self-reporting and are therefore inherently subjective. In this study we show that a comprehensive analysis of physical activity (PA) under real life conditions may capture behavioral aspects that may reflect physical and emotional functioning.
  
  
  METHODS
  PA was monitored during five consecutive days in 60 chronic pain patients and 15 pain-free healthy subjects. To analyze the various aspects of pain-related activity behaviors we defined the concept of PA 'barcoding'. The main idea was to combine different features of PA (type, intensity, duration) to define various PA states. The temporal sequence of different states was visualized as a 'barcode' which indicated that significant information about daily activity can be contained in the amount and variety of PA states, and in the temporal structure of sequence. This information was quantified using complementary measures such as structural complexity metrics (information and sample entropy, Lempel-Ziv complexity), time spent in PA states, and two composite scores, which integrate all measures. The reliability of these measures to characterize chronic pain conditions was assessed by comparing groups of subjects with clinically different pain intensity.
  
  
  CONCLUSIONS
  The defined measures of PA showed good discriminative features. The results suggest that significant information about pain-related functional limitations is captured by the structural complexity of PA barcodes, which decreases when the intensity of pain increases. We conclude that a comprehensive analysis of daily-life PA can provide an objective appraisal of the intensity of pain.
authors.affiliation: Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. anisoara.ionescu@epfl.ch
authors.forename: Anisoara
authors.initials: A
authors.lastname: Paraschiv-Ionescu
authors.forename: Christophe
authors.initials: C
authors.lastname: Perruchoud
authors.forename: Eric
authors.initials: E
authors.lastname: Buchser
authors.forename: Kamiar
authors.initials: K
authors.lastname: Aminian
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D019540:Area Under Curve; D001519:Behavior; D059350:Chronic Pain / Q000175:diagnosis*; D001330:Electronic Data Processing; D004644:Emotions; D019277:Entropy; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008490:Medical Informatics / Q000295:instrumentation* / Q000379:methods; D008875:Middle Aged; D015233:Models, Statistical; D009043:Motor Activity; D010147:Pain Measurement / Q000295:instrumentation* / Q000379:methods*; D011446:Prospective Studies; D012372:ROC Curve; D012189:Retrospective Studies