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.

Methodology: 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.

Conclusion: 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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Examples of PA patterns represented as symbolic/numerical sequences (left panel) or color barcodes (right panel)

: (A) and (B) have a similar distribution of states but differ in their sequential structure. The pattern shown in (C) differs from (A) and (B) by both, the distribution/variety of states and their sequential structure.

Figure 2. Examples of PA barcodes recorded in two aged-matched subjects: a chronic pain patient (A) and a healthy pain free subject (B):

the two barcodes differ in both, the variety of PA states and their temporal distribution. The suggestion is that the chronic pain patient was not able to dynamically alternate between various body movements/activities, probably because of pain intensity and/or other factors such as fear of movement and activity avoidance.

Figure 3. Metrics characterizing PA barcode (mean±SD)

: structural-static complexity quantified by normalized information entropy (Hn), (A); structural-dynamic complexity quantified by Sample entropy (SampEn) and Lempel-Ziv complexity (LZC), (B), (C); classical PA metric quantifying the percent of time spent in activity (walking and standing, i.e. PAS = 3 to 18) (D); composite deterministic score (CDS) which integrates all defined metrics (E).

Figure 4. Correlations between metrics characterizing PA barcode.

Figure 5. Receiver operator characteristics (ROC) curves and area under the curve (AUC) for the composite deterministic score (CDS) and the composite statistical score (CSS):

No Pain, vs. Severe Pain in the Middle Age groups (A) and Moderate Pain vs. Severe Pain in the Old Age groups (B).

Figure 6. Quantitative assessment of intense physical activity states, PAS (mean±SD)

: No Pain vs. Severe Pain in the Middle Age groups (A) and Moderate Pain vs. Severe Pain in the Old Age groups (B). These results indicated that elderly with either low pain or high pain levels are not able to perform very intense activities.