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Barcoding human physical activity to assess chronic pain conditions.

Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K - PLoS ONE (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. anisoara.ionescu@epfl.ch

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.

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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).
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pone-0032239-g003: 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).

Mentions: The analysis showed that all defined PA metrics decreased when pain intensity increased as illustrated in Fig. 3 (mean±SD). The information entropy Hn (Fig. 3A) showed very significant differences between the Middle Age groups with about 74% non-overlap. The same trend was observed between the Old Age groups, but the differences were not statistically significant (28% non-overlap).


Barcoding human physical activity to assess chronic pain conditions.

Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K - PLoS ONE (2012)

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).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3285674&req=5

pone-0032239-g003: 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).
Mentions: The analysis showed that all defined PA metrics decreased when pain intensity increased as illustrated in Fig. 3 (mean±SD). The information entropy Hn (Fig. 3A) showed very significant differences between the Middle Age groups with about 74% non-overlap. The same trend was observed between the Old Age groups, but the differences were not statistically significant (28% non-overlap).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. anisoara.ionescu@epfl.ch

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.

Show MeSH
Related in: MedlinePlus