Startseite Tonic cuff pressure pain sensitivity in chronic pain patients and its relation to self-reported physical activity
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Tonic cuff pressure pain sensitivity in chronic pain patients and its relation to self-reported physical activity

  • Olof Skogberg EMAIL logo , Linn Karlsson , Emmanuel Bäckryd und Dag Lemming
Veröffentlicht/Copyright: 14. Dezember 2023
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Scandinavian Journal of Pain
Aus der Zeitschrift Scandinavian Journal of Pain Band 24 Heft 1

Abstract

Objectives

Physical inactivity is a global health concern and a significant problem among chronic pain patients. They often experience pain flare-ups when they try to increase their physical activity level. Most research on the relationship between pain sensitivity and physical activity has been on healthy participants. Data on chronic pain patients are lacking. Using cuff pressure algometry, this study investigated tonic cuff pressure pain sensitivity and its associations to self-reported physical activity and other patient-reported outcomes in chronic pain patients.

Methods

Chronic pain patients (n=78) were compared to healthy controls (n=98). Multivariate data analysis was used to investigate the associations between tonic cuff pressure pain sensitivity, physical activity, and other patient-reported outcome measures.

Results

The three most important variables for group discrimination were perceived health status (EQVAS: p(corr)=−0.85, i.e., lower in patients), depression (HADS-D: p(corr)=0.81, i.e., higher in patients), and the tonic cuff pressure pain sensitivity variable maximum pain intensity (VAS-peak-arm: p(corr)=0.75, i.e., higher in patients). In patients, the most important predictors for high VAS-peak-arm were female sex (p(corr)=−0.75), higher number of painful regions (p(corr)=0.72), higher pain intensity (p(corr)=0.55), followed by lower level of self-reported physical activity (p(corr)=−0.39). VAS-peak-arm in patients correlated negatively with self-reported physical activity (rho=−0.28, p=0.018).

Conclusions

Physical activity may be the most important patient-changeable variable correlating to pain sensitivity. This study highlights the importance of more research to further understand how increased physical activity may decrease pain sensitivity in chronic pain patients.

Keywords: cuff pressure pain sensitivity; pain assessment; patient-reported outcome measures; physical activity

Introduction

Chronic pain with moderate to severe pain intensity affects about 20 % of the adult population [1]. Physical activity (PA) reduces the risk of chronic pain [2] and prescribed exercise significantly relieves symptoms in most pain conditions [2]. In addition, there is also evidence for prescribing exercise for managing many diseases such as cardiovascular diseases [3]. Exercise is a subset of PA characterised by planned, structured, and repetitive PA [4]. Exercise is considered an important component of effective chronic pain management and it is well established that long-term exercise training is beneficial [2, 5, 6]. Exercise is the only treatment for fibromyalgia that received a “strong for” recommendation in the latest European (EULAR) guidelines [7].

However, and paradoxically, while a single bout of aerobic or resistance exercise may lead to exercise-induced hypoalgesia (EIH) in healthy controls [8], [9], [10], for chronic pain patients it may, on the contrary, be less efficient or even increase pain intensity [5, 10]. A more enduring hypoalgesia has been suggested as a feature associated with increased levels of habitual PA for healthy individuals [11]. To optimize the benefits of PA, it is important to understand how EIH works for healthy individuals and why it may be impaired in people with chronic pain [12].

The most commonly proposed mechanism of EIH among healthy is enhanced descending inhibition by activation of the opioid and cannabinoid systems [12, 13]. Among chronic pain patients with widespread pain EIH is often dysfunctional [8]. For these patients the risk of their pain getting worse in a flare-up when they try to start with exercise [14] is an important barrier for the caregiver to understand. This may be an important factor in the lack of long-term adherence with exercise as an intervention [15]. There are numerous biological and cognitive factors that contribute to pain, so changes in any one or more of these by exercise could account for EIH [13]. It is not clear, however, what these mechanisms are and how they differ between healthy individuals and individuals with chronic pain.

Computerized cuff pressure algometry (CPA) [16], [17], [18], [19] can be used to asses pain thresholds [20, 21] as well as dynamic features of pain modulation such as temporal summation, i.e., tonic cuff pressure pain sensitivity, conditioned pain modulation (CPM) and spatial summation of pain [22]. CPA mainly assesses sensitivity in deep somatic tissue, is less baised by inter- and intraexaminer variability than conventional handheld pressure algometry [23], and can contribute to understand the relationship between PA and chronic pain.

CPM represents one type of endogenous pain inhibition and refers to the process whereby one noxious stimulus inhibits or reduces the perception of a second noxious stimulus. CPM provides an index of the strength of pain inhibition. Using these paradigms, EIH would manifest as a reduction in temporal summation and/or an increase in CPM [13]. Deficits in CPM have been observed among patients with a variety of chronic musculoskeletal, visceral, and neuropathic pain conditions [24]. Studying temporal aspects and CPM among chronic pain patients might therefore contribute to the understanding of EIH and more enduring hypoalgesia in this group of patients. Previous CPA studies found increased pressure pain sensitivity in fibromyalgia [16], whiplash associated disorder [17], lateral epicondylalgia [18], and chronic pain after revision knee arthroplasty [19]. Previous studies have shown that sensitivity to experimental pressure pain is associated with sex, pain intensity, number of painful regions and to some extent habitual PA [11, 20, 23, 25]. Different types of exercise have been reported to reduce pain sensitivity [26, 27], but unfortunately most studies have been done on small samples of healthy males. However, a dose response relationship was found between self-reported PA and pain sensitivity in chronic pain patients [11].

The aim of the study was to explore the multivariate associations between tonic cuff pressure pain sensitivity, habitual self-reported PA and patient-reported outcomes (including pain intensity) in chronic pain patients, first by comparing them with healthy controls and then by an in-depth analysis of patient data. Our hypothesis was that there would be a negative correlation between tonic cuff pressure pain sensitivity and self-reported PA.

Methods

Study participants

The chronic pain patients included underwent an Interdisciplinary Pain Rehabilitation Program (IPRP) at the Pain and Rehabilitation Centre, University Hospital, Linköping, Sweden. Consecutive inclusion was used, and screening failures (i.e., evaluated for participation but not included) and/or dropouts were not registered. In total, 78 patients with different chronic pain conditions (>3 months) were included, please see previous publication [21] for diagnoses. The following general inclusion criteria for IPRP were used: disabling chronic pain (on sick leave or experiencing major interference in daily life due to chronic pain); age between 18 and 65 years; no further medical investigations needed. General exclusion criteria from IPRP included severe psychiatric morbidity, abuse of alcohol and/or drugs, diseases that did not allow physical exercise, or presence of clinical indicators of a possible serious underlying condition. Additional specific exclusion criteria for this study were: compartment syndrome; neuropathic pain with allodynia; severe mental illness which would have made it impossible to join the intervention (investigator’s judgment); pregnancy; language difficulties; pain duration shorter than three months; medication with anticoagulants.

This article is the fourth report using data from a sample of healthy individuals [20], [21], [22]. The 98 controls were recruited through advertisement in the local newspaper. Inclusion criteria were age between 20 and 65 years, and pain-free. A brief medical history was taken that excluded any current or previous presence of a pain condition. The patients and the controls were not matched for age and gender.

The study was conducted in accordance with the Declaration of Helsinki. The study was granted ethical clearance by the Linköping University Ethics Committee (2011/102-31). All participants were given written information about the study and consented to participate.

Study procedures

Details about patient-reported and healthy control-reported outcome measures (PROMs) have been published previously [21]. Briefly, we recorded three basic demographic data (age, sex, Body Mass Index, BMI) and the following 7 PROMs in all participants:

Godin Leisure-Time Exercise Questionnaire (GLTEQ) to estimate the habitual PA level [28], [29], [30]; a high score indicates higher intensity and higher frequency of weekly leisure-time activities. Hospital anxiety and depression scale (HADS) – HADS_A and HADS_D [31]. European quality of life instrument (EQ5D) captures a person’s perceived health status. Only the second part of this instrument, EQ-VAS, was used. The patient marks his self-perceived health on a 100-point scale, a “thermometer”, with defined endpoints where high values indicate good health and low values poor health [32]. Anxiety Sensitivity Index (ASI); high scores indicate high levels of anxiety [33]. General Self-Efficacy Scale (GSES) contains 10 questions that evaluate the perception of confidence in one’s own ability. The sum of the points is between 10 and 40, where a higher sum represents a better outcome [34]. Quality of Life Scale (QOLS-S), a higher total score showing a higher satisfaction. The item scores are added to a total score, ranging from 16 to 112 [35].

Moreover, in patients, we assessed Pain intensity on a 0–10 numeric rating scale; anatomical extent of pain by a pain drawing encompassing 36 anatomical regions (Painful regions; possible range: 0–36); pain duration in months.

Tonic cuff pressure sensitivity tests were performed as follows. The experimental setup consisted of a double-chamber 13 cm wide tourniquet cuff (a silicone high-pressure cuff, separated lengthwise into two equal-size chambers; VBM Medizintechnik GmbH, Sulz, Germany), a computer-controlled air compressor, and an electronic visual analogue scale (NociTech and Aalborg University, Denmark). The cuff was connected to the compressor and wrapped around the heads of biceps and triceps muscles of the arm or around the mid-portion of the triceps surae muscles of the leg. The dominant “writing hand” side was chosen for all assessments. All assessments were made in a single session. Cuff algometry with first single- and then double-chamber cuffs was completed on the arm and then on the leg. All assessments were repeated twice at each site, and the mean was calculated for further analyses. A short (<5 min) break was allowed when switching the cuff from arm to leg. The maximum pressure limit used was 25 kPa in both regions and maintained for 10 min. Cuff inflation to 25 kPa pressure was instantaneous, as well as the deflation after 10 min. The stimulation could be aborted at any time by the subject using a push button or the experimenter via the computer.

During cuff pressure stimulation the pain intensity was simultaneously recorded using a 10 cm electronic visual analogue scale (VAS) updating 10 times per second. The subject adjusted the VAS score via a variable lever, and the magnitude was displayed on a red-light bar fully visible to the subject. Zero and 10 cm extremes on the VAS were defined as “no pain” and as “worst possible pain,” respectively. The VAS was recorded for 12 min (i.e. including 2 min recording with zero pressure). The maximum pain intensity (VAS-peak) measuring temporal aspects and time to VAS-peak were extracted. If a subject aborted the 12 min assessment prematurely, the time elapsed (sec) was registered (aborttime). Areas under the VAS-curve (AUC) were calculated based on raw data for the 10 min with cuff inflation. Hence, 6 cuff algometry variables were used in the present study: VAS-peak-arm, VAS-peak-leg, AUC-arm, AUC-leg, aborttime-arm, and aborttime-leg. Please see supplementary material, Appendix S1, for Figure S1 regarding VAS-peak.

Conditioned pain modulation (CPM) was calculated as the absolute change in pressure pain thresholds (PPT) values (PPT-15 – PPT0) between before and after cuff deflation (conditioning stimuli), based on practice recommendations for CPM testing [36]) PPTs (test stimuli) were determined using a manual pressure algometer (Somedic AB, Sweden) mounted with a probe (with a contact area of 1 cm2) on the muscle belly of the ipsilateral tibialis anterior muscle. The pressure was increased by 30 kPa/s until the subject perceived pain and pushed a stop-button. The PPT was defined as the mean of two trials obtained with minimum 30 s interval. The PPT was measured at baseline during the initial part of the experiment, immediately before the tonic stimulation (PPT-0), after 2 min of tonic cuff stimulation (PPT-2) and 15 min (PPT-15) after beginning tonic stimulation of the arm (i.e. 3 min after ending the continuous VAS recording).

Statistics

Unless stated otherwise data are presented in the text as median (interquartile range). For multivariate data analysis by projection (MVDA), SIMCA-P+ (version 16, Umetrics AB, Umeå, Sweden) was used. Principal component analysis (PCA) and orthogonal projections to latent structures – discriminant analysis (OPLS-DA) were used, as well as OPLS. Briefly, PCA is an unsupervised technique that models the correlation structure of a dataset, and thereby enables identification of multivariate outliers and identification of prominent subgroups. OPLS-DA, which is a supervised technique, was used for group comparisons, enabling the identification of the X-variables (i.e., predictors) most responsible for group discrimination while at the same time taking the whole correlation structure of the material into consideration. X-variables with absolute values of p(corr)>0.4 are usually considered “significant”. p(corr) are the new variable values visualized in the loading plot, scaled as a correlation coefficient (ranging from −1.0 to +1.0) between model and original data. For each OPLS model, R2 describes the goodness of fit and Q2 describes goodness of prediction. Cross validated analysis of variance (CV-ANOVA) with a p≤0.05 was used to validate the obtained model. Detailed information on the MVDA methodology has been published elsewhere [37, 38].

IBM Statistical Package for the Social Sciences (SPSS, IBM Corporation, Somers, NY, USA) version 27.0 was used to compare groups by Kruskal Wallis test and Mann-Whitney U test. Spearman’s rho was used for bivariate correlations [39]. p≤0.05 was considered statistically significant in all tests, with no adjustment for multiple comparisons. Effect sizes was calculated as Cohen’s d. Cohen’s d of 0.20–0.49 was considered a small effect size, 0.50–0.79 a medium effect size, and ≥0.80 a large effect size [40].

Results

Group differences

Univariate statistics

There were significant differences between patients and controls in age, BMI, cuff algometry variables, PA (GLTEQ) and other PROMs (Table 1). There was no significant difference between the groups with respect to gender (Table 1). The effect sizes by Cohen’s d were large, with the largest for perceived health status (EQVAS), depression (HADS-D), and VAS-peak arm (2.34, 1.77, and 1.68, respectively) (Table 1).

Table 1:

Demographic data, cuff algometry data and patient-reported outcome measures (PROMs) for patients and controls.

Variables Controls (n=98) Patients (n=78) Statistics (p-Value) Effect size by Cohen’s d
Demographic data
Age 30 (26–44) 43(35–50) <0.001 0.69
Gender (% females) 51 % 61.5 % 0.16 N.A.
BMI 23.8 (22–25.5) 24.9 (23.5–28) 0.002 0.56
Cuff algometry data
VAS-peak-arm (0–10) 2.2 (0.7–4.5) 8.8 (6–10) <0.001 1.68
AUC-arm 528.3 (136.3–1,328.3) 3,765.3 (1896.9–5,285.8) <0.001 1.59
VAS-peak-leg (0–10) 2.7 (0.7–4.6) 8.9 (6.1–10) <0.001 1.53
AUC-leg 642.5 (85.4–1700.4) 3,870 (1722.5–5,145.8) <0.001 1.46
Patient-reported outcome measures (PROMs)
Pain intensity (0–10) N.A. 6 (5–7) N.A. N.A.
Painful regions (0–36) N.A. 13(7–18) N.A. N.A.
Pain duration, months N.A. 33.5(24–120) N.A. N.A.
GLTEQ 45.5 (28.8–63.5) 31 (19.5–49) 0.001 0.5
QOLS 92 (84–98) 74.5 (61–84) <0.001 1.35
GSES 32 (28.8–35) 27 (23.5–31) <0.001 0.87
HADS-A 3 (1–5) 7 (4–10.5) <0.001 1.12
HADS-D 1 (0–3) 7 (4–10) <0.001 1.77
ASI 8 (6–12) 17 (10–26) <0.001 0.8
EQVAS 90 (80–95) 50 (33.5–65) <0.001 2.34

  1. Data are expressed as median (25th–75th percentiles) except for gender. ASI, anxiety sensitivity index; AUC, area under VAS-curve; EQ-VAS, the second part of the European quality of life instrument and captures a person’s perceived health status; GLTEQ, Godin Leisure-time exercise questionnaire; GSES, the general self-efficacy scale; HADS-A and HADS-D, anxiety and depression subscale of hospital anxiety and depression scale; QOLS, quality of life scale.

Multivariate regression of group belonging

A PCA was performed on all subjects taken together (n=176) on the following 16 variables: age, sex, BMI, 7 PROMs available in all participants and 6 cuff algometry variables. One strong moderate outlier was found and that individual (a patient) was excluded from further analyses. The new resulting PCA (n=175, 2 PC, R2=0.49, Q2=0.32) had no outliers. All 16 variables were then included as X-variables in an OPLS-DA using group belonging (patients vs. controls) as dependent variable (i.e., a dichotomous Y-variable). Clear group separation was achieved (Figure 1) and the model was highly significant (Table 2). The three most important variables for differentiating patients from controls were perceived health status (EQVAS: p(corr)=−0.85 i.e., lower in patients), depression (HADS-D: p(corr)=0.81 i.e., higher in patients), and maximum pain intensity (VAS-peak-arm: p(corr)=0.75 i.e., higher in patients) (Table 2).

Figure 1: Score plot of the OPLS-DA model, illustrating group separation between patients (1, blue dots) and controls (0, green dots) using 16 X-variables: age, sex, BMI, 7 PROMs available in all participants and 6 cuff algometry variables. The two axes represent the two latent variables of the model. Class separation between patients and controls occurs along the t[1] axis (inter-class variation), whereas the to[1] axis represents intra-class variation. OPLS-DA, orthogonal partial least squares–discriminant analysis.
Figure 1:

Score plot of the OPLS-DA model, illustrating group separation between patients (1, blue dots) and controls (0, green dots) using 16 X-variables: age, sex, BMI, 7 PROMs available in all participants and 6 cuff algometry variables. The two axes represent the two latent variables of the model. Class separation between patients and controls occurs along the t[1] axis (inter-class variation), whereas the to[1] axis represents intra-class variation. OPLS-DA, orthogonal partial least squares–discriminant analysis.

Table 2:

Variable importance for group discrimination (patients vs. controls) in descending order of absolute p(corr) values, in orthogonal partial least squares – discriminant analysis (OPLS-DA) model.

Variables p(corr)
EQVAS −0.85
HADS-D 0.81
VAS-peak-arm 0.75
VAS-peak-leg 0.73
QOLS −0.70
AUC-arm 0.59
AUC-leg 0.58
HADS-A 0.56
GSES −0.49
Age 0.45
ASI 0.40
BMI 0.38
GLTEQ −0.33
Aborttime-arm −0.33
Aborttime-leg −0.33
Sex −0.05
N 175
R 2 0.70
Q 2 0.67
CV-ANOVA P-value <0.001

  1. |p(corr)|>0.4 was considered significant; for an explanation of p(corr), see the Statistics section. Positive p(corr) values signify higher levels in patients than in controls, and vice versa. ASI, anxiety sensitivity index; EQ-VAS, the second part of the European quality of life instrument and captures a person’s perceived health status, GLTEQ, Godin Leisure-time exercise questionnaire; GSES, the general self-efficacy scale HADS-A and HADS-D, anxiety and depression subscale of hospital anxiety and depression scale; QOLS, quality of life scale; AUC, area under VAS – curve. At the four bottom rows are presented n, R2, goodness of fit, Q2, goodness of prediction, and CV-ANOVA p value, p-value for the cross-validated analysis of variance (CV-ANOVA).

In depth analyses of patient data

Multivariate regression of VAS-peak-arm

As one of the most important variables discriminating between patients and controls was maximum pain intensity (VAS-peak-arm) (Table 2), we in the next step regressed this variable (Y-variable) in patients (missing value in one patient, hence n=76) using demographic data and all 10 PROMs available in patients (i.e., including the three pain variables only available in patients) – hence in total 13 X-variables. This was done to better understand the influence of these variables on VAS-peak-arm. The most important predictors for higher VAS-peak-arm were female sex, high number of painful regions, high pain intensity, and lower self-reported PA (GLTEQ) (Table 3).

Table 3:

Variable importance for regression of VAS-peak-arm for patients in descending order of absolute p(corr) values in orthogonal partial least squares (OPLS) model.

Variables p(corr)
Sexa −0.75
Painful regions 0.72
Pain intensity 0.55
GLTEQ −0.39
Pain duration 0.30
GSES −0.19
Age −0.24
QOLS 0.12
ASI −0.05
BMI 0.05
EQVAS −0.04
HADS-A 0.03
HADS-D 0.04
N 76
R 2 0.42
Q 2 0.32
CV-ANOVA P-value <0.001

  1. aFemale sex is associated with higher VAS-peak. |p(corr)|>0.4 was considered significant; for an explanation of p(corr), see the statistics section. A positive p(corr) signifies a positive correlation with VAS-peak-arm. ASI, anxiety sensitivity index; EQ-VAS, the second part of the European quality of life instrument and captures a person’s perceived health status; GLTEQ, Godin Leisure-time exercise questionnaire; GSES, the general self-efficacy scale; HADS-A and HADS-D, anxiety and depression subscale of hospital anxiety and depression scale; QOLS, quality of life scale. At the four bottom rows are presented n, R2, goodness of fit, Q2, goodness of prediction, and CV-ANOVA P value, P-value for the Cross validated analysis of variance (CV-ANOVA).

Further investigations of VAS-peak-arm, sex, and self-reported PA

VAS-peak-arm correlated with number of painful regions (rho=0.44, p<0.001), pain intensity (rho=0.34, p=0.002) and negatively with GLTEQ (rho=−0.28, p=0.018). There was no significant bivariate correlation between VAS-peak-arm and pain duration, GSES, QOLS EQVAS, HADS-A, or HADS-D. Female patients (n=46) had higher VAS-peak arm than male patients (n=30) (10.0 (7.9–10.0) vs. 6.1 (3.7–8.6), p<0.001). As in a previous study [22], based on a median split, patients were dichotomized into lower (GLTEQ≤32) and higher (GLTEQ>32) self-reported PA. Four groups were defined based on sex and dichotomized GLTEQ: men with a high activity level (MHA), men with a low activity level (MLA), women with a high activity level (WHA), and women with a low activity level (WLA). As can be seen in Table 4, there was a statistical difference between the four groups regarding age, VAS-peak-arm, AUC-arm, VAS-peak-leg, AUC-leg, pain intensity, and number of painful regions. When looking at men and women separately, neither PROMS nor cuff algometry variables differed between higher and lower levels of PA – see Tables 4 and 5 (column furthest to the left for men and furthest to the right for women). On the other hand, in participants with the same level of PA, there were many significant differences between men and women – see MLA vs. WLA, and MHA vs. WHA, in Table 5.

Table 4:

Demographic data, cuff algometry data and patient-reported outcome measures (PROMs) in four groups: men with a high activity level (MHA), men with a low activity level (MLA), women with a high activity level (WHA) and women with a low activity level (WLA).

Variables MHA (n=16) MLA (n=13) WHA (n=20) WLA (n=23) Statistics
Demographic data
Age 46.5(40.3–57.0) 47.0(35.5–52.5) 43.5(25.3–47.3) 38(25.0–47.0) 0.048
BMI 25.4(24.6–27.4) 26.8(23.5–32.4) 24.9(23.7–28.0) 23.8(21.6–28.1) 0.40
Cuff algometry data
VAS-peak-arm (0–10) 5.17(3.4–7.3) 7.8(4.0–8.97) 10.0(7.52–10.0) 10.0(8.9–10.0) <0.001
Aborttime-arma 600.0(600.0–600.0) 600.0(600.0–600.0) 600.0(256.3–600.0) 600.0(545.0–600.0) 0.089
AUC-arm 1999.9(1,074.5–3,571.2) 3,546.6(1913.6–4,810.5) 4,037.7(385.2–6,166.3) 5,211.4(3,564.1–5,982.5) 0.006
VAS-peak-leg (0–10) 5.9(4.5–8.1) 8.2(4.1–9.7) 10.0(7.1–10.0) 10.0(7.6–10.0) 0.005
Aborttime-legb 600.0(600.0–600.0) 600.0(502.5–600.0) 600.0(386.3–600.0) 600.0(50.0–600.0) 0.27
AUC-leg 2,291.9(1,595.0–3,404.5) 3,181.1(1,027.8–4,423.2) 4,706.2(1,540.3–5,602.0) 5,091.3(3,958.7–5,976.2) 0.001
Patient-reported outcome measures (PROMs)
Pain intensity (0–10) 5.0(4.3–6.8) 4.0(3.5–6.0) 6.0(5.0–7.0) 6.0(5.0–7.0) 0.007
Painful regions (0–36) 7.0(4.0–14.3) 9.0(7.0–15.5) 14.0(9.25–26.5) 15.0(11.0–18.0) 0.011
Pain duration, months 29.5(14.0–145.5) 42.0(24.0–132) 30.0(15.0–105.0) 32.0(24.0–145.0) 0.84
QOLS 72.0(56.0–94.0) 76.0(59.5–82.0) 76.5(59.0–86.0) 77.0(68.5–83.5) 0.91
GSES 30.5(21.0–35.8) 29.0(25.0–30.5) 26.0(24.0–30.0) 25.0(22.0–30.0) 0.31
HADS-A 7.0(2.3–11.5) 7.0(1.5–10.0) 6.5(4.0–10.0) 8.0(6.0–10.0) 0.75
HADS-D 8.0(2.0–12.3) 8.0(5.5–12.5) 7.0(5.3–9.8) 7.0(4.0–10.0) 0.82
ASI 14.5(9.25–24.5) 13.0(6.5–19.5) 18.0(10.25–25.0) 21.0(10.0–29.0) 0.36
EQVAS 52.5(36.3–70.0) 50.0(27.5–52.0) 56.0(30.8–73.8) 50.0(40.0–60.0) 0.45

  1. Data are expressed as median (25th–75th percentiles). Statistics computed by Kruskal Wallis Test. Posthoc statistics are presented in Table 4. Bold text denotes significant group difference, ASI, anxiety sensitivity index; AUC, area under VAS-curve; EQ-VAS, the second part of the European quality of life instrument and captures a person’s perceived health status; GSES, the general self-efficacy scale, HADS-A and HADS-D, anxiety and depression subscale of hospital anxiety and depression scale; MHA, men with high activity level; MLA, men with low activity level; WHA, women with high activity level; WLA, women with low activity level; QOLS, quality of life scale. a2 MHA patients aborted before 600 s, as well as 1 MLA patient, 8 WHA patients, and 6 WLA patients. b2 MHA patients aborted before 600 s, as well as 2 MLA patients, 6 WHA patients, and 9 WLA 9 patients.

Table 5:

Posthoc p-values for significant group differences in Table 4.

Variable MHA vs. MLA Cohen’s d MHA vs. WHA Cohen’s d MHA vs. WLA Cohen’s d MLA vs. WHA Cohen’s d MLA vs. WLA Cohen’s d WHA vs. WLA Cohens d
Age p=0.44 p=0.058 p=0.13 p=0.29 p=0.10 p=0.31
VAS-peak-arm p=0.12 p<0.001 −1.2 p<0.001 −1.9 p=0.33 p=0.04 −1.2 p=0.81
AUC-arm p=0.06 p=0.22 p=0.001 −1.6 p=1 p=0.021 −0.9 p=0.22
VAS-peak-leg p=0.24 p=0.10 p=0.020 −1.1 p=0.82 p=0.047 −0.7 p=0.76
AUC-leg p=0.62 p=0.42 p<0.001 −2.4 p=0.18 p=0.004 −1.6 p=0.25
Pain intensity (0–10) p=0.14 p=0.19 p=0.059 p=0.009 −1.1 p=0.001 −1.4 p=0.57
Painful regions (0–36) p=0.12 p=0.004 −1.0 p=0.09 p=0.10 p=0.17 p=0.79

  1. Posthoc statistics by Mann Whitney U test. For median (25th–75th percentiles) values, see Table 3. Bold text denotes significant group difference. Effect size was calculated as Cohen’s d on variables with a significant difference. AUC, area under VAS- curve; MHA, men with high activity level; MLA, men with low activity level; WHA, women with high activity level; WLA, women with low activity level.

CPM

The conditioning stimulus VAS-peak was 2.2 (0.7–4.5) for the controls and 8.9 (6.1–10.0) for the patients, and a putative CPM effect was therefore only investigated in patients. The CPM effect in patients was 0.0 (−40.1 to +32.1), i.e. implying a median value of 0 for CPM. When comparing CPM in the four groups of patients defined on the basis of sex and GLTEQ, there was no significant difference between the groups (p=0.55), Figure 2. There was no significant difference between the highly active patients (GLTEQ>32) and normally active patients (GLTEQ≤32) (0.0 (−39.3 to 19.3) vs. −7.8 (−46.6 to 33.5), p=1.0 respectively).

Figure 2: Difference in Conditioned Pain Modulation (CPM) between groups based on sex and self-reported PA level. MHA, men with high activity level; MLA, men with low activity level; WHA, women with high activity level; WLA, women with low activity level.
Figure 2:

Difference in Conditioned Pain Modulation (CPM) between groups based on sex and self-reported PA level. MHA, men with high activity level; MLA, men with low activity level; WHA, women with high activity level; WLA, women with low activity level.

Discussion

Main findings

This study showed that during tonic cuff algometry the maximum pain intensity (VAS-peak-arm) in chronic pain patients, as shown with bivariate statistics, was negatively correlated with self-reported PA (GLTEQ) and not significantly associated with GSES, QOLS EQVAS, HADS-A or HADS-D. Also, when using MVDA, self-reported PA (GLTEQ) was a more important predictor for higher VAS-peak-arm than GSES, QOLS, EQVAS, HADS-A or HADS-D. The tonic cuff stimulation reflects the integration over time of the deep pressure pain sensation and can be regarded as equivalent to temporal summation of a repeated pain stimulus [41]. VAS-peak-arm in this case is the maximum pain sensation during tonic stimulation and the VAS-peak is a sign of facilitated temporal summation. The area under VAS-curve can also be used but is hampered by subjects aborting the stimulation in advance. This result supports the finding in our previous study [21] in which self-reported PA was associated with pain sensitivity. Although much is known about exercise and its association with pain sensitivity [2, 5, 6], there is limited research available regarding the effect of PA measured by self-reported PA and its association with pain sensitivity. The results in this study are in line with the findings of Årnes et al., who showed a dose response relationship between self-reported PA and pain sensitivity in chronic pain patients [11]. Since temporal summation paradigms provide information mostly about facilitatory mechanisms underlying nociceptive processes [13], these findings indicate that the level of PA is correlated with facilitatory mechanisms. However, when investigating the respective effects of sex and self-reported PA, sex was found to be far more important (Tables 4 and 5) which previously also was seen among healthy controls [22]. This difference of pain sensitivity related to sex is in line with previous studies [11, 20, 23, 25].

Cuff pressure algometry (CPA)

CPA mainly assesses sensitivity in deep somatic tissue and is less biased by inter- and intra-examiner variability than conventional handheld pressure algometry [23]. We have previously shown in nonathletic healthy subjects that CPA-assessed pain detection level (i.e., the pain threshold) is associated with both sex and PA levels [20], and that tonic cuff pressure pain sensitivity, assessing the temporal profile of cuff pain [42], was not associated with level of PA in healthy subjects [22]. When we compared this cohort with patients, we have also previously shown that pressure pain tolerance thresholds (PPT), as well as degree of depression and perceived health status, discriminated between patients and healthy controls, and there was an association between pain tolerance and level of self-reported PA in patients [21].

The results of this and our previous study [21] show that self-reported PA level is associated with pain sensitivity, measured both as tonic (temporal aspects) in the present study and as phasic (measuring PTT) in our previous study [21]. CPA offers a method to measure the association between long-term changes in PA, pain sensitivity (including temporal summation and CPM) in patients with chronic pain as a complement to measuring EIH. Vaegter and Graven-Nielsen [43] have shown that patients with facilitated pronociceptive mechanisms (temporal summation) and impaired antinociceptive mechanisms (CPM) had significantly more pain areas, indicating that both less-efficient CPM and temporal summation may be important indicators of widespread pain. A more enduring hypoalgesia has been suggested as a feature associated with increased levels of habitual PA for healthy individuals [11] and more knowledge is needed both regarding healthy and chronic pain patients. Please see supplementary material, Appendix S2, for further discussion regarding the CPM results of the present study.

Possible clinical implications in the future

The results of this and our previous study [21] show that higher self-reported PA level is associated with lower pain sensitivity. However, given the cross-sectional and observational nature of the studies, we still do not know if patients can influence their pain sensitivity by increasing their PA level. Exercise is considered an important component of effective chronic pain management and it is well-established that long-term exercise training provides pain relief [5]. A systematic review [44] regarding EIH indicates that when exercising at a low-to-moderate intensity nociplastic pain patients may have a small effect on EIH even if the effect is low. However, if the intensity level is too high, many chronic pain patients experience pain flare-ups and do not adhere to prescribed exercise programs. To further examine if patients can influence their pain sensitivity by increasing their PA level, it would be helpful to follow a group of patients and study their pain sensitivity and PA over time and see how they relate to each other. In future studies, it would be interesting to examine if patients are able to increase their PA after an intervention such as IPRP and if this affects their pain sensitivity and other variables.

In the future, we speculate that it might be possible to use CPA VAS-peak values (or other methods to measure pain sensitivity) to inform patients about their optimal level of PA, and thereby individually tailor exercise programs for chronic pain patients on a low to medium intensity. This could help caregivers to adjust the initial intensity of exercise at the individual level to minimize the risk for pain flare-ups. If further research shows that patients are able to decrease VAS-peak by increasing level of PA, it could also be worth following VAS-peak longitudinally to adjust PA slowly and to have it as an objective goal for the patient. The present study can at best be seen as a small step towards such a “precision medicine” use of CPA.

The possibility of modulating pain sensitivity by PA in chronic pain patients should not be discarded, and it is important to study the complex relationships between pain sensitivity and PA. If patients are able to increase their PA, they will also decrease their risk of many other conditions such as cardiovascular diseases [3]. In a recent study [45] with 475,171 participants in the UK Biobank, chronic pain was shown to be an underestimated cardiovascular risk factor. It was also highlighted that, as a risk factor for cardiovascular disease, chronic pain was comparable with that of diabetes. This highlights the importance of more research and more resources to help these patients.

Limitations

Firstly, a limitation that hampers the generalisability of the study is that screening failures and dropouts were not registered prospectively (i.e., the possibility of a selection bias). The chronic pain patients included however underwent an IPRP at our Pain and Rehabilitation Centre and there is nothing that indicates that patients included differ from the ordinary patients participating an IPRP. Secondly, cross-sectional studies have obvious drawbacks, and longitudinal studies are warranted to follow the long-term effect of an intervention such as IPRP. Moreover, if there is indeed a causal effect between PA and pain sensitivity, we still do not know the direction of causality. In future studies it would be interesting to examine if pain sensitivity decreases if patients are able to increase their PA after an intervention such as IPRP. Thirdly, there are limitations pertaining to the assessment of PA by questionnaires. There are many different questionnaires to choose from, and it can be questioned if GLTEQ was the right choice to measure PA and if it is sensitive enough in patients with chronic pain even if it is so among healty controls. When reading the literature, it is confusing how exercise and PA often are used interchangeably. For instance, E in GLTEQ stands for exercise, but in the original article [29] regarding GLTEQ one can read that “the reliability and concurrent validity of a simple questionnaire to access leisure time physical activity has been investigated”. The GLTEQ has by Godin himself also been called Godin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [30] which might have been a better name. Further, it has been claimed that measuring PA with a questionnaire like GLTEQ is not as reliable as for example accelerometers [46]. Accelerometer is a feasible large-scale alternative to energy expenditure estimation as a gold standard [47]. In further studies it would be beneficial to assess PA with both accelerometer and self-reported questionnaires. Fourthly, the heterogeneity of pain diagnoses reflects the group of patients in IPRPs, but the heterogeneity can also be viewed as a limitation. To be able to better interpret the results of a specific pain diagnosis, it would be favourable to only have patients with the same diagnosis. Fifthly, the patients and the controls were not age and gender matched which may have affected the results. Finally, a deeper understanding of how PA affects pain sensitivity should include the use of different biomarkers, e.g., concerning the relationship between pain and chronic inflammation [48].

Conclusions

The results indicate that the level of PA may be the most important patient-changeable variable that correlated to pain sensitivity. This study highlights the importance of more research to further understand if increased PA may decrease pain sensitivity in chronic pain patients.

Corresponding author: Olof Skogberg, MD, Pain and Rehabilitation Center and Department of Health, Medicine and Caring Sciences, Linköping University, SE-581 85 Linköping, Sweden, E-mail: , Phone: +46 101030000
Emmanuel Bäckryd and Dag Lemming co-last authors.

Acknowledgments

The authors thank Eva-Britt Lind and Ulrika Wentzel Olausson for their help and assistance with performing measurements and acquiring data.

  1. Research ethics: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as amended in 2013), The study was granted ethical clearance by the Linköping University Ethics Committee (2011/102-31).

  2. Informed consent: Informed consent has been obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: Olof Skogberg, Linn Karlsson, Emmanuel Bäckryd and Dag Lemming have no conflicts of interest to declare.

  5. Research funding: ALF Grants, Region Östergötland (EB), NEURO Sweden (EB), Research Grants Region Östergötland (OS), and a Research grant, Sinnescentrum, Region Östergötland (OS).

  6. Data availability: There is no ethical permission to make data available.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sjpain-2023-0033).

Received: 2023-03-16
Accepted: 2023-11-09
Published Online: 2023-12-14

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  48. Relationship between perfectionism, overactivity, pain severity, and pain interference in individuals with chronic pain: A cross-lagged panel model analysis
  49. Access to psychological treatment for chronic cancer-related pain in Sweden
  50. Validation of the Danish version of the knowledge and attitudes survey regarding pain
  51. Associations between cognitive test scores and pain tolerance: The Tromsø study
  52. Healthcare experiences of fibromyalgia patients and their associations with satisfaction and pain relief. A patient survey
  53. Video interpretation in a medical spine clinic: A descriptive study of a diverse population and intervention
  54. Role of history of traumatic life experiences in current psychosomatic manifestations
  55. Social determinants of health in adults with whiplash associated disorders
  56. Which patients with chronic low back pain respond favorably to multidisciplinary rehabilitation? A secondary analysis of a randomized controlled trial
  57. A preliminary examination of the effects of childhood abuse and resilience on pain and physical functioning in patients with knee osteoarthritis
  58. Differences in risk factors for flare-ups in patients with lumbar radicular pain may depend on the definition of flare
  59. Real-world evidence evaluation on consumer experience and prescription journey of diclofenac gel in Sweden
  60. Patient characteristics in relation to opioid exposure in a chronic non-cancer pain population
  61. Topical Reviews
  62. Bridging the translational gap: adenosine as a modulator of neuropathic pain in preclinical models and humans
  63. What do we know about Indigenous Peoples with low back pain around the world? A topical review
  64. The “future” pain clinician: Competencies needed to provide psychologically informed care
  65. Systematic Reviews
  66. Pain management for persistent pain post radiotherapy in head and neck cancers: systematic review
  67. High-frequency, high-intensity transcutaneous electrical nerve stimulation compared with opioids for pain relief after gynecological surgery: a systematic review and meta-analysis
  68. Reliability and measurement error of exercise-induced hypoalgesia in pain-free adults and adults with musculoskeletal pain: A systematic review
  69. Noninvasive transcranial brain stimulation in central post-stroke pain: A systematic review
  70. Short Communications
  71. Are we missing the opioid consumption in low- and middle-income countries?
  72. Association between self-reported pain severity and characteristics of United States adults (age ≥50 years) who used opioids
  73. Could generative artificial intelligence replace fieldwork in pain research?
  74. Skin conductance algesimeter is unreliable during sudden perioperative temperature increases
  75. Original Experimental
  76. Confirmatory study of the usefulness of quantum molecular resonance and microdissectomy for the treatment of lumbar radiculopathy in a prospective cohort at 6 months follow-up
  77. Pain catastrophizing in the elderly: An experimental pain study
  78. Improving general practice management of patients with chronic musculoskeletal pain: Interdisciplinarity, coherence, and concerns
  79. Concurrent validity of dynamic bedside quantitative sensory testing paradigms in breast cancer survivors with persistent pain
  80. Transcranial direct current stimulation is more effective than pregabalin in controlling nociceptive and anxiety-like behaviors in a rat fibromyalgia-like model
  81. Paradox pain sensitivity using cuff pressure or algometer testing in patients with hemophilia
  82. Physical activity with person-centered guidance supported by a digital platform or with telephone follow-up for persons with chronic widespread pain: Health economic considerations along a randomized controlled trial
  83. Measuring pain intensity through physical interaction in an experimental model of cold-induced pain: A method comparison study
  84. Pharmacological treatment of pain in Swedish nursing homes: Prevalence and associations with cognitive impairment and depressive mood
  85. Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators – A case–control study
  86. The effect of social exclusion on pain perception and heart rate variability in healthy controls and somatoform pain patients
  87. Revisiting opioid toxicity: Cellular effects of six commonly used opioids
  88. Letter to the Editor
  89. Post cholecystectomy pain syndrome: Letter to Editor
  90. Response to the Letter by Prof Bordoni
  91. Response – Reliability and measurement error of exercise-induced hypoalgesia
  92. Is the skin conductance algesimeter index influenced by temperature?
  93. Skin conductance algesimeter is unreliable during sudden perioperative temperature increase
  94. Corrigendum
  95. Corrigendum to “Chronic post-thoracotomy pain after lung cancer surgery: a prospective study of preoperative risk factors”
  96. Obituary
  97. A Significant Voice in Pain Research Björn Gerdle in Memoriam (1953–2024)
https://doi.org/10.1515/sjpain-2023-0033
Schlagwörter für diesen Artikel
cuff pressure pain sensitivity; pain assessment; patient-reported outcome measures; physical activity
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Editorial Comment
  2. From pain to relief: Exploring the consistency of exercise-induced hypoalgesia
  3. Christmas greetings 2024 from the Editor-in-Chief
  4. Original Articles
  5. The Scandinavian Society for the Study of Pain 2022 Postgraduate Course and Annual Scientific (SASP 2022) Meeting 12th to 14th October at Rigshospitalet, Copenhagen
  6. Comparison of ultrasound-guided continuous erector spinae plane block versus continuous paravertebral block for postoperative analgesia in patients undergoing proximal femur surgeries
  7. Clinical Pain Researches
  8. The effect of tourniquet use on postoperative opioid consumption after ankle fracture surgery – a retrospective cohort study
  9. Changes in pain, daily occupations, lifestyle, and health following an occupational therapy lifestyle intervention: a secondary analysis from a feasibility study in patients with chronic high-impact pain
  10. Tonic cuff pressure pain sensitivity in chronic pain patients and its relation to self-reported physical activity
  11. Reliability, construct validity, and factorial structure of a Swedish version of the medical outcomes study social support survey (MOS-SSS) in patients with chronic pain
  12. Hurdles and potentials when implementing internet-delivered Acceptance and commitment therapy for chronic pain: a retrospective appraisal using the Quality implementation framework
  13. Exploring the outcome “days with bothersome pain” and its association with pain intensity, disability, and quality of life
  14. Fatigue and cognitive fatigability in patients with chronic pain
  15. The Swedish version of the pain self-efficacy questionnaire short form, PSEQ-2SV: Cultural adaptation and psychometric evaluation in a population of patients with musculoskeletal disorders
  16. Pain coping and catastrophizing in youth with and without cerebral palsy
  17. Neuropathic pain after surgery – A clinical validation study and assessment of accuracy measures of the 5-item NeuPPS scale
  18. Translation, contextual adaptation, and reliability of the Danish Concept of Pain Inventory (COPI-Adult (DK)) – A self-reported outcome measure
  19. Cosmetic surgery and associated chronic postsurgical pain: A cross-sectional study from Norway
  20. The association of hemodynamic parameters and clinical demographic variables with acute postoperative pain in female oncological breast surgery patients: A retrospective cohort study
  21. Healthcare professionals’ experiences of interdisciplinary collaboration in pain centres – A qualitative study
  22. Effects of deep brain stimulation and verbal suggestions on pain in Parkinson’s disease
  23. Painful differences between different pain scale assessments: The outcome of assessed pain is a matter of the choices of scale and statistics
  24. Prevalence and characteristics of fibromyalgia according to three fibromyalgia diagnostic criteria: A secondary analysis study
  25. Sex moderates the association between quantitative sensory testing and acute and chronic pain after total knee/hip arthroplasty
  26. Tramadol-paracetamol for postoperative pain after spine surgery – A randomized, double-blind, placebo-controlled study
  27. Cancer-related pain experienced in daily life is difficult to communicate and to manage – for patients and for professionals
  28. Making sense of pain in inflammatory bowel disease (IBD): A qualitative study
  29. Patient-reported pain, satisfaction, adverse effects, and deviations from ambulatory surgery pain medication
  30. Does pain influence cognitive performance in patients with mild traumatic brain injury?
  31. Hypocapnia in women with fibromyalgia
  32. Application of ultrasound-guided thoracic paravertebral block or intercostal nerve block for acute herpes zoster and prevention of post-herpetic neuralgia: A case–control retrospective trial
  33. Translation and examination of construct validity of the Danish version of the Tampa Scale for Kinesiophobia
  34. A positive scratch collapse test in anterior cutaneous nerve entrapment syndrome indicates its neuropathic character
  35. ADHD-pain: Characteristics of chronic pain and association with muscular dysregulation in adults with ADHD
  36. The relationship between changes in pain intensity and functional disability in persistent disabling low back pain during a course of cognitive functional therapy
  37. Intrathecal pain treatment for severe pain in patients with terminal cancer: A retrospective analysis of treatment-related complications and side effects
  38. Psychometric evaluation of the Danish version of the Pain Self-Efficacy Questionnaire in patients with subacute and chronic low back pain
  39. Dimensionality, reliability, and validity of the Finnish version of the pain catastrophizing scale in chronic low back pain
  40. To speak or not to speak? A secondary data analysis to further explore the context-insensitive avoidance scale
  41. Pain catastrophizing levels differentiate between common diseases with pain: HIV, fibromyalgia, complex regional pain syndrome, and breast cancer survivors
  42. Prevalence of substance use disorder diagnoses in patients with chronic pain receiving reimbursed opioids: An epidemiological study of four Norwegian health registries
  43. Pain perception while listening to thrash heavy metal vs relaxing music at a heavy metal festival – the CoPainHell study – a factorial randomized non-blinded crossover trial
  44. Observational Studies
  45. Cutaneous nerve biopsy in patients with symptoms of small fiber neuropathy: a retrospective study
  46. The incidence of post cholecystectomy pain (PCP) syndrome at 12 months following laparoscopic cholecystectomy: a prospective evaluation in 200 patients
  47. Associations between psychological flexibility and daily functioning in endometriosis-related pain
  48. Relationship between perfectionism, overactivity, pain severity, and pain interference in individuals with chronic pain: A cross-lagged panel model analysis
  49. Access to psychological treatment for chronic cancer-related pain in Sweden
  50. Validation of the Danish version of the knowledge and attitudes survey regarding pain
  51. Associations between cognitive test scores and pain tolerance: The Tromsø study
  52. Healthcare experiences of fibromyalgia patients and their associations with satisfaction and pain relief. A patient survey
  53. Video interpretation in a medical spine clinic: A descriptive study of a diverse population and intervention
  54. Role of history of traumatic life experiences in current psychosomatic manifestations
  55. Social determinants of health in adults with whiplash associated disorders
  56. Which patients with chronic low back pain respond favorably to multidisciplinary rehabilitation? A secondary analysis of a randomized controlled trial
  57. A preliminary examination of the effects of childhood abuse and resilience on pain and physical functioning in patients with knee osteoarthritis
  58. Differences in risk factors for flare-ups in patients with lumbar radicular pain may depend on the definition of flare
  59. Real-world evidence evaluation on consumer experience and prescription journey of diclofenac gel in Sweden
  60. Patient characteristics in relation to opioid exposure in a chronic non-cancer pain population
  61. Topical Reviews
  62. Bridging the translational gap: adenosine as a modulator of neuropathic pain in preclinical models and humans
  63. What do we know about Indigenous Peoples with low back pain around the world? A topical review
  64. The “future” pain clinician: Competencies needed to provide psychologically informed care
  65. Systematic Reviews
  66. Pain management for persistent pain post radiotherapy in head and neck cancers: systematic review
  67. High-frequency, high-intensity transcutaneous electrical nerve stimulation compared with opioids for pain relief after gynecological surgery: a systematic review and meta-analysis
  68. Reliability and measurement error of exercise-induced hypoalgesia in pain-free adults and adults with musculoskeletal pain: A systematic review
  69. Noninvasive transcranial brain stimulation in central post-stroke pain: A systematic review
  70. Short Communications
  71. Are we missing the opioid consumption in low- and middle-income countries?
  72. Association between self-reported pain severity and characteristics of United States adults (age ≥50 years) who used opioids
  73. Could generative artificial intelligence replace fieldwork in pain research?
  74. Skin conductance algesimeter is unreliable during sudden perioperative temperature increases
  75. Original Experimental
  76. Confirmatory study of the usefulness of quantum molecular resonance and microdissectomy for the treatment of lumbar radiculopathy in a prospective cohort at 6 months follow-up
  77. Pain catastrophizing in the elderly: An experimental pain study
  78. Improving general practice management of patients with chronic musculoskeletal pain: Interdisciplinarity, coherence, and concerns
  79. Concurrent validity of dynamic bedside quantitative sensory testing paradigms in breast cancer survivors with persistent pain
  80. Transcranial direct current stimulation is more effective than pregabalin in controlling nociceptive and anxiety-like behaviors in a rat fibromyalgia-like model
  81. Paradox pain sensitivity using cuff pressure or algometer testing in patients with hemophilia
  82. Physical activity with person-centered guidance supported by a digital platform or with telephone follow-up for persons with chronic widespread pain: Health economic considerations along a randomized controlled trial
  83. Measuring pain intensity through physical interaction in an experimental model of cold-induced pain: A method comparison study
  84. Pharmacological treatment of pain in Swedish nursing homes: Prevalence and associations with cognitive impairment and depressive mood
  85. Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators – A case–control study
  86. The effect of social exclusion on pain perception and heart rate variability in healthy controls and somatoform pain patients
  87. Revisiting opioid toxicity: Cellular effects of six commonly used opioids
  88. Letter to the Editor
  89. Post cholecystectomy pain syndrome: Letter to Editor
  90. Response to the Letter by Prof Bordoni
  91. Response – Reliability and measurement error of exercise-induced hypoalgesia
  92. Is the skin conductance algesimeter index influenced by temperature?
  93. Skin conductance algesimeter is unreliable during sudden perioperative temperature increase
  94. Corrigendum
  95. Corrigendum to “Chronic post-thoracotomy pain after lung cancer surgery: a prospective study of preoperative risk factors”
  96. Obituary
  97. A Significant Voice in Pain Research Björn Gerdle in Memoriam (1953–2024)
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