Conceived and designed the experiments: API CP EB KA. Performed the experiments: API. Analyzed the data: API. Contributed reagents/materials/analysis tools: API. Wrote the paper: API CP EB KA.
The authors have declared that no competing interests exist.
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.
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.
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.
Pain is one of the major universal experiences of human beings defined by the International Association for the Study of Pain (IASP) as ‘
Despite significant progress to understand pain mechanisms, the assessment of chronic pain in clinical practice remains a major challenge that involves multidimensional outcome domains such as
Physical activity (PA) has many dimensions that can be characterized and quantified, such as the
When PA is monitored over long periods of time, parameters related to the different dimensions can be used to define
Based on the above hypothesis and background, the present study is a further step in the investigation of the dynamics of patterns (sequences) generated from a succession of various
We performed a retrospective analysis on data that were collected prospectively in an observational longitudinal study designed to assess the PA in chronic pain patients treated with spinal cord stimulation (SCS). After approval of the ethical committee of the University of Lausanne, Switzerland, and written informed consent was obtained, 60 patients suffering from chronic pain caused by failed back surgery syndrome (n = 21), spinal stenosis (n = 19), peripheral vascular disease (n = 8), and combined pathologies (n = 12) were enrolled. All patients reported pain-related limitations of their walking perimeter and were candidates for SCS therapy. All patients were referred to the Pain Management Centre of the Hospital of Morges, Switzerland because of persistent pain despite optimal medical management. As the main inclusion criterion was the eligibility for SCS treatment, the group was not homogeneous in terms of pathologies and demographic characteristics. A group of 15
Pain was measured using a visual analogue scale (VAS) from 0 to 10. All subjects were asked to rate the
The pain intensity was categorized as ‘
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Pain intensity (0 to 10) | 0 | 7±1.3 | 3.6±1.4 | 7.7±1.3 | ||
Age (yr) | 57±14 | 54±9 | 71±14 | 74±8 | ||
Gender, |
8(53%) | 15(60%) | 9(56%) | 10(52%) | ||
Height (m) | 168±3 | 167±7 | 168±5 | 168±8 | ||
Weight (kg) | 71±10 | 76±24 | 72±13 | 76±12 | ||
BMI (kg/m2) | 24.6±2.5 | 26.7±7.2 | 25.1±3.3 | 26.5±3.7 | ||
Employed, n (%) | 13(86%) | 25(100%) | 2(0.13%) | 0(0%) | ||
Diagnosis, n (type) | - | 4(SS) 13(FBSS) 2(CRPS) 3(PVD) 1(LB) 1(PN) 1(HD) | - | 6(SS) 4(FBSS) 3(PVD) 1(LB) 1(Meralgya) 1(DA) | 9(SS) 4(FBSS) 2(PVD) 1(PN) 1(DA) 2(HD) | - |
The effect of pain intensity on PA was compared on age-matched groups, i.e.
Diagnosis:
The monitoring of PA was performed under free living conditions using three miniaturized data-loggers (55×40×18 mm, 50 g) stuck to the skin with medical adhesive patches (Coloplast Systems, Denmark) and Velcro (Velcro®,USA). The data-loggers are custom designed from commercial inertial sensors (bi-axial accelerometers, ADXL202, ±2 g and uni-axial gyroscope, ADXRS300, 300°/sec), memory, electronics for data acquisition and rechargeable batteries. One device was fixed on the sternum to measure the trunk vertical and frontal accelerations, and the angular velocity in the sagittal plane. Two devices were fixed on one leg aligned with the medio-lateral axis of the thigh and shank, to measure vertical and frontal accelerations and the angular velocity of thigh and shank in the sagittal plane
The basic idea of PA barcoding was to combine different PA dimensions in order to define PA states. A numerical symbol was assigned to each PA states so that the motor activity behavior during the observation period appeared encoded in a sequence of symbols. The sequence was then analyzed to provide PA metrics and was represented as a color barcode to provide global illustrative visual information.
We estimated the
According to the algorithms developed previously
The parameters related to the type, the intensity, and the duration were combined within successive time-windows of one-second duration to define the various PA states. A numerical symbol was assigned to each PA states using the encoding procedure illustrated in
if the type of PA was identified as
if the type of PA was identified as
The choice of thresholds used for trunk acceleration norm i.e.
if the type of PA was identified as
The thresholds used for walking cadence i.e.
This encoding procedure provided the representation of the patterns of PA as successions of 18 possible states. Mathematically, such representation corresponds to symbolic sequences over the alphabet
: (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.
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The PA symbolic sequences/barcodes to be analyzed were obtained by concatenation of data from the five recording days. Each data point/sample of the PA barcode represents a PA state calculated for time-windows of one second, so that the length of the analyzed barcodes (
The choice of a measure of complexity must be based on its ability to reveal clinically relevant features of movement behavior and to discriminate between experimental groups. The illustrative examples in
Structural-static measures allow the quantification of the amount of different PA states while structural-dynamic measures permit the quantification of the amount of different states in the sequence and a description of transitions/succession between states. Structural-dynamic measures are said to be ‘sequence-sensitive’ because their values depend on the order of PA states in the sequence
We used three complementary complexity measures to quantify the information embedded in the PA barcodes, and to investigate the effectiveness of the proposed methodology to differentiate between chronic pain conditions (intensities): the
As a simple illustrative example, when applying the algorithm described in
The examples above illustrate that the entropy
Although both,
The ‘traditional’ assessment of PA and functional capacity of subjects is expressed in terms of the time spent walking and/or standing (e.g. estimated globally as % over the monitoring time)
When several metrics quantifying various specific aspects of PA are available, it is possible to combine them into a single parameter that may increase the ability of PA barcode to discriminate patients with high versus low chronic pain intensity. We studied and compared two approaches:
The
The
The impact of clinically different pain intensities on PA was compared between age-matched groups, i.e.
The ability of the composite PA scores (CDS and CSS) to differentiate between the groups of subjects was quantified and compared using the receiver operator characteristic (ROC) curve and the area under the curve (AUC). The AUC is a summary measure of differentiation accuracy, lying in the range (0.5, 1), with 1 indicating perfect discrimination and 0.5 indicating no discrimination capacity
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.
The analysis showed that all defined PA metrics decreased when pain intensity increased as illustrated in
: structural-static complexity quantified by normalized
The sample entropy
The %
The correlations illustrated in
This study suggests that specific patterns of PA (‘
The essential feature of the barcode concept is to ‘
The information entropy,
:
The sample entropy,
The composite scores
An important observation from the results presented in
There is growing evidence that chronic pain is associated with physical and psychological impairments that results in muscular disuse, anxiety and decreased quality of life
Recent results in
A key issue in pain behavioral research is whether (and how) pain and pain-related fear affect the activities of
There are several potential limitations regarding the interpretation of the present findings. A first limitation is that the relatively small sample size in each group may have led to under-powered statistical comparisons. A second limitation is that the retrospective cross-sectional nature of the study precluded a perfect matching between groups. While the groups were matched by age and occupational status (working or retired), they were inhomogeneous in terms of individuals' occupation type (profession) and pain mechanism or diagnosis. However as all patients had a pain-related limitation in their walking perimeter, the significance of the results is not expected to be affected. Finally, a more generic limitation is that for neither the “traditional” nor for the newly developed metrics there is (yet) an agreed definition of normal values and normal range. Similarly the clinical significance of the modifications that are observed remains to be established. Larger prospective and controlled studies are therefore needed to define normal PA, using sophisticated complexity metrics, which are needed to properly characterize chronic pain conditions whether in terms of the intensity of pain or possibly in terms of features that are disease-specific.
Pain has long been regarded as a diagnostic feature. The classical semiology of urethral colitis due to renal stone teaches that patients suffering from renal colic are “frantically” restless which is very different from patients with peritonitis who remain as immobile as possible to avoid pain. Similarly, patients with painful lower extremity neuropathy tend to move around as much as they can, while patients with hip arthritis tend to remain in the same position and avoid walking, which would increase pain. Hence pain does affect behavior (and PA) in a predictive way, irrespective of the intensity of the symptom.
Yet the clinical appraisal of behavioral patterns is crude and the traditional metrics are not contributive. The use of PA metrics that precisely and completely characterize the features of various chronic pain disorders may substantially improve our current assessment in a number of ways. Since it appears that the
Another potential useful application of PA barcoding is the assessment of patients who have communication difficulties such as the elderly or the cognitively impaired
The authors would like to thank the nurses of Anesthesia and Pain Management Department, Hospital of Morges, Switzerland, for their assistance in recording the data in the pain patients and healthy subjects of this study. They also wish to thank the reviewers for their helpful comments on the original manuscript.