1. Introduction
Central sensitization (CS) is defined by the International Association for the Study of Pain (IASP) as “increased responsiveness of nociceptive neurons in the central nervous system to either normal or subthreshold afferent input.”25,53,131 Central sensitization has been linked with a multitude of chronic pain disorders in humans,62,70,79,105,110,144 143 100 quantitative sensory testing (QST) is used to assess the dynamic modulation of nociceptive signals, which can suggest the presence of CS.6,137 Quantitative sensory testing measures include pain threshold tests, temporal summation (a measure of “wind-up” or enhancement of pain with prolonged nociceptive exposure31 27
Although QST allows for a comprehensive assessment of pain sensitivity profiles, it often involves select training, expensive laboratory equipment, and additional patient burden, which limits its use in clinical settings.104
A self-report questionnaire that is widely used in the assessment of central sensitization is the Central sensitization Inventory89 74
The Pain Sensitivity Questionnaire (PSQ), however, may more directly measure the sensory facilitation involved in CS.107 80,108
It would appear that the term “central sensitization ” has undergone construct drift from its canonical (preclinical) use describing enhanced responsivity of central nociceptive neurons64,139,140 central sensitization is unclear. As such, we are interested in exploring the degree to which the CSI and PSQ reflects CS as an “increased responsiveness of nociceptive neurons.” The meta-analyses of available studies examined whether self-report measures (the PSQ or CSI) aligned more closely with QST measures or experimental measures of nociceptive sensitivity or psychological questionnaires such as anxiety, depression, stress, etc. In doing so, we aim to assess and compare the 2 questionnaires for the degree to which they assess nociceptive sensitization or emotional sensitization.
2. Methods
The review protocol has been previously published1 https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=208731 Meta-Analyses statement.95
2.1. Sources of evidence
We searched MEDLINE, PsycINFO, and Web of Science from their inception until June 2021. Two separate searches were conducted including both the CSI and PSQ. One search reviewed these questionnaires for sensory correlates (eg, quantitative sensory testing ). The second search reviewed their correlations with psychological questionnaires (eg, anxiety, depression, pain catastrophising, etc). Any duplicates within both searches were removed.
2.2. Search terms
2.2.1. Search 1 terms
(“Quantitative Sensory Testing ” or “wind-up” or “temporal summation” or “conditioned pain modulation” or “pain threshold” or “pain ratings” or “hyperalgesia” or “allodynia” or “offset analgesia” or “widespread pain” or “evoked pain” or “experimental pain” or “pain tolerance”) AND (“central sensitization inventorycentral sensitization inventorypain sensitivity questionnaire ”).
2.2.2. Search 2 terms
(“depression” or “anxiety” or “stress” or “catastrophizing” or “rumination” or “neuroticism” or “personality” or “abuse” or “trauma”) AND (“central sensitization inventorycentral sensitization inventorypain sensitivity questionnaire ”).
2.3. Inclusion criteria
Only human studies were eligible for inclusion. They must have been written in English and an original peer-reviewed experiment (ie, not a dissertation, case study, or review article). Finally, studies must have included at least one of the CSI or PSQ instruments. The CSI or PSQ must have been correlated against at least one psychological or sensory measure of interest.
2.4. Types of studies
The review included studies that correlate the PSQ or CSI with sensory or psychological measures. In studies that involved an intervention, measures were only considered if they were assessed at baseline.
2.5. Data collection, extraction, and management
Two independent reviewers assessed studies for eligibility (G.R.A. and W.G.). Initially, titles and abstracts were screened using excel, and full-text screening was performed on citations felt to be potentially eligible. Both authors were required to agree for inclusion. We excluded studies that did not satisfy our inclusion criteria. Discrepancies between the reviewers were resolved by discussion and consensus, with a third reviewer being consulted in cases of disagreement. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of this process is provided (Fig. 1 ).
Figure 1.: Flowchart to show the process of inclusion eligibility for meta-analyses . CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
2.6. Outcome Measures
Our primary outcomes were correlations between the CSI total score and the PSQ total score with sensory measures associated with CS. These include QST measures: temporal summation, pain thresholds and tolerance, and any measure related to nociceptive hypersensitivity or widespread pain. We were also interested in exploring the extent to which self-report questionnaires may be predictive of descending aspects of modulation (impaired inhibition), therefore, including conditioned pain modulation as a measure of interest.
The second outcome measure was correlations between the CSI and the PSQ and psychological factors. These included questionnaires that assess depression, anxiety, stress, pain catastrophizing, abuse, trauma, mindfulness, neuroticism, or personality and any other measure related to emotional hypersensitivity.
2.7. Data extraction and management
One reviewer (G.R.A.) extracted relevant data from each study (correlation coefficient r and number of participants n). If these values were not given within the paper, authors were contacted to provide the relevant correlation coefficient. A second reviewer checked the extracted data (D.W.-A.). Data extracted from each citation included information about the study design and the correlation coefficients age, sex, and number/type of participants.
2.8. Data items
2.8.1. Psychological measures
Psychological correlates with PSQ or CSI that were provided for at least 3 studies were included in meta-analyses ; these were depression, anxiety, pain catastrophising, stress, sleep, and kinesiophobia. The questionnaires used to assess depression were the Depression Anxiety Stress Scale (DASS-21),67 125 146 14 114 97 101 71 67 125 146 14 81 34 71 94 67 68 125 138 3 2 87 126
Other questionnaires that reported correlations with CSI or PSQ in fewer than 3 studies were included for narrative review but not for meta-analyses ; these were the Brief Resilience Scale (BRS),120 59 15 10 13 24 75 11 111
2.8.2. Nociceptive sensory measures
Sensory correlates suitable for meta-analyses included pressure pain threshold (PPT), heat pain threshold (HPT), conditioned pain modulation (CPM), temporal summation (TS), and widespread pain. Methods that were not performed regularly enough for meta-analyses (ie, fewer than 3 studies reported correlations for these measures) included suprathreshold (pain rating associated with the fifth heat pulse out of a series of 5 heat pulses [46°C]),23 119 33,55,60,99,128,129,138 70,86 38 103 107 https://links.lww.com/PAIN/B753
The methodologies for the nociceptive measures included in the meta-analyses were considered standardised and generally consistent, although they did show slight variations in some cases, as outlined below:
2.8.3. Pressure pain threshold
In most cases for PPT, a digital or handheld mechanic pressure algometer was used to apply pressure through a 1 cm2 probe. The main variation in PPT was in the rate of pressure applied being between 0.1 kg/second and 1 kg/second. The body site for which the PPT was applied varied across studies, although it was consistently applied to muscles. Participants were asked to report the moment that the increasing pressure became painful or unpleasant.
2.8.4. Heat pain threshold
For heat pain threshold, a thermode was applied to various body sites depending on the study, consistently applied to muscles. In all studies, the thermode started at a baseline temperature (commonly 32°C) and was increased at a varied rate between 0.5°C/second and 2°C/second across studies. Participants were asked to report the moment that the increasing heat became painful or unpleasant.
2.8.5. Conditioned pain modulation
For CPM, the conditioning stimulus was most regularly a cold-water bath applied to the opposite hand of the test stimulus. One study used a thermode to elicit cold pain,37 103 60 103 98
2.8.6. Temporal Summation
Temporal summation was often calculated by applying a predetermined pressure intensity using a pressure algometer to a body site (body sites varied across studies) for several repetitions (commonly 10 consecutive stimulations) at a rate of 1/second. The first pain rating in the series was subtracted from the final pain rating to give a TS score. One study used this same paradigm, as well as a similar paradigm using electrical stimulation.60 37
2.8.7. Widespread Pain
Widespread pain measures consisted of the Widespread Pain Index (WPI)32 16
2.8.8. Phasic heat
Phasic heat stimulation was administered at 47°C or 48°C for 5 seconds. The average of 4 ratings collected at the set temperature of 47/48°C was used as a phasic pain rating. If the phasic heat rating was performed at 2 temperatures, the average of these 2 scores was taken as the phasic heat score.
2.8.9. Tonic cold
A thermode surface oscillated around 3°C (0.5 Hz, amplitude: ±1°C) for 60 seconds. The average of the ratings over the 4-time points and 4 measurements (twice on each hand) was used as the tonic cold pain rating.
2.8.10. Pinprick stimulation
Pain-intensity ratings of pinprick stimuli were obtained from the volar forearm using a weighted pinprick stimulator (force: 512 mN).
2.8.11. Assessment of risk of bias in included studies
For each study included in the review, a modified version of the quality appraisal process proposed by Hayden et al.40
2.8.12. Analysis of participation bias
Specific sources of participation bias (bias assessment category (1)) were identified, i.e., age and sex characteristics of the population sample, as well as population samples that excluded participants based on mental health, physical health, or medication being taken.
2.9. Synthesis of results
2.9.1. Data eligibility
Sample sizes (n) and correlation scores (r ) on primary outcomes were extracted from papers or by responses collected from emailing corresponding authors and presented to show individual study characteristics. Only data that provided sample size and a correlation value were included. Only Spearman rank and Pearson correlation (r -values) were considered eligible so that meta-analyses could be conducted.
2.9.2. Data preparation
Central Sensitization InventoryMeta-analyses were conducted if at least 3 studies reported findings for a related construct. Each meta-analysis was made up of a combination of instruments for that construct, eg, correlates with CSI and anxiety will include CSI against STAI Trait and State, the Anxiety component of the Depression Anxiety stress scale (DASS-21), the anxiety component of the Hospital Anxiety and Depression Scale (HADS), etc.
Meta-analyses for quantitative sensory measures were conducted for each measure separately: Pressure pain threshold, heat pain threshold, conditioned pain modulation, and temporal summation were separately meta-analysed against CSI. If studies reported 2 correlations for one construct (eg, multiple body sites taken for pain threshold or trait, and state anxiety scores were reported for anxiety), scores were averaged together.
2.9.3. Data visualization
Data for individual studies were reported in Supplementary Tables 1a-d (available at https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 Meta-analyses findings were presented in the table format with a summary of the effect size on the right of the table. For visualisation purposes, forest plots were provided for some of the main findings (forest plots for all other meta-analyses can also be found in Supplementary Figures 1a-n, available at https://links.lww.com/PAIN/B753
2.9.4. Primary analysis
Meta-analyses consisted of sample sizes (n) and correlation scores (r ) on primary outcomes extracted from papers or by response from emailing corresponding authors and will be presented in a table. These values were then input into a meta-analysis to give a weighted mean correlation using the Hunter–Schmidt method.113 122
2.9.5. Tests for heterogeneity
The χ2 test was used to measure statistical heterogeneity across studies. Significant heterogeneity (P < 0.05) is an indicator of clinical and methodological heterogeneity. The I2 statistic was calculated to report heterogeneity as a percentage.29,43 43 43
3. Results
After excluding duplicates, the initial literature search identified 175 articles (Fig. 1 ). Forty-nine of these articles were eligible after full screening. A further 74 corresponding authors were emailed for specific correlations (either the PSQ or CSI with our variables of interest) as data were collected but not reported within their publication. Twenty authors responded and provided the correlates of interest. Therefore, a total of 69 studies were eligible for quality appraisal or bias assessment (see Fig. 1 for further details).
3.1. Risk of bias
All 69 studies included in the review went through an assessment of quality and risk of bias. The results are presented in Figure 2 . An additional breakdown of participation bias is presented in Figure 3 .
(1) Participation bias—Many studies showed participation bias. This reflects some common exclusion criteria that occur in studies that measure pain sensitivity, eg, patients with neuropathic pain are often excluded as well as individuals with any mental health disorders or people taking any medication as this may affect their responsivity to stimuli. We ran a separate assessment to showcase the common characteristics which were excluded from study populations. The “other” column was primarily made up of participants being excluded due to cognitive deficits (Fig. 3 ).
(2) Missing data—A few studies reported missing data. This was in part due to incomplete questionnaires or participation dropout. Data not being reported for all the participants (eg, reporting for pain patients but not healthy controls) were reflected in their bias assessment.
(3) Standardized method—Most studies conducted a standardized method. The questionnaires were standardized for CSI, PSQ, and psychological correlates, and if sensory measures were taken, the method was of standardised quality. We could check this by assessing whether studies used protocols from previously published work or how homogenous protocols were across studies. Two studies were excluded due to a lack of methodological standardization.76,133
(4) Desirable statistics—Most studies provided Pearson correlations for our variables of interest, but a few provided Spearman rank correlations. If the study had undergone the relevant methodology but did not provide Pearson or Spearman correlations, we contacted the authors to provide data for one of these correlations. Synthesised results could then be analysed comparatively and included in meta-analyses . One study gave partial correlations and was, therefore, excluded.80
(5) Sample size—63 of 69 studies had an adequate sample size—a power calculation based on r = 0.3 indicated that the sample size should have been 43 or over. Anything lower than n = 43 was flagged. However, all studies were included for weighted meta-analyses . Low sample sizes simply reflected less influence in a weighted meta-analysis. However, these small sample sizes may be relevant when reviewing less frequent correlations that were not suitable for meta-analysis, ie, narrative review.
Figure 2.: Bias assessment (quality assessment).
Figure 3.: Bias assessment (participation bias).
Overall, the quality assessment for studies selected for this study showed that the studies were of good quality with a low risk of bias. Three studies were excluded due to a nonstandardised method being used or data not being given in the required format for correlational meta-analyses .76,80,133
There were some concerns over participation bias (Fig. 3 ) due to certain types of patients with chronic pain being excluded in several studies (n = 46) based on their medical condition, eg, neuropathy. Patients on pain medication were excluded in several studies (n = 19), patients with mental health or psychiatric disorders were excluded in studies (n = 24), and individuals with cognitive deficits were excluded in a number of studies (n = 12). These exclusion criteria may appear biased, but it is typical across studies that examine pain populations as medical conditions, medication, cognitive deficits, and mental health can all interfere with the pain processing or interpretation of pain.51,65,130
Only one study was identified as high risk in any of the assessed domains on participation bias. The reasons for this were that in one study of a Chinese population, women taking contraception were excluded.99
3.2. Excluded studies
Two studies were excluded due to a lack of methodological standardization,76,133 80
3.3. Summary
All studies included in this review were assessed for methodology and were considered standardized by at least 2 reviewers. Although it should be noted, in general, there was a certain level of heterogeneity in methodology across studies involving nociceptive sensory measures, particularly with CPM.
3.4. Primary outcomes
The surviving 66 studies included a total of 13,284 participants. 7470 participants took the Central sensitization InventoryPain Sensitivity Questionnaire across 30 different studies (one study included both questionnaires).23 https://links.lww.com/PAIN/B753
Supplementary Tables 1a-d (available at https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 https://links.lww.com/PAIN/B753 102 63,72
3.5. Meta analyses
The results of the meta-analyses are reported in Table 1 . Studies reporting data for subset samples instead of a total (eg, patients and healthy controls) were entered into the meta-analyses as separate correlations as they reflected separate populations. In a separate subanalysis, they were divided appropriately to compare findings for pain patients with healthy participants (Table 2 ). Data for some of the main findings are presented as forest plots in Figures 4A–H . Forest plots for all other meta-analyses can be found in Supplementary Figures 1a-n (available at https://links.lww.com/PAIN/B753
Table 1 -
Meta-analyses for
Central Sensitization Inventory and
Pain Sensitivity Questionnaire with psychological and sensory measures.
Outcome measures
Studies
No. of populations
Total number of participants
Weighted mean correlation (95% CI)
Heterogeneity
Strength of correlation
All participants—CSI
Depression
4,19,23,41,48,50,73,83–85,90–92,117,118
15
4248
0.58 (0.54 to 0.62)
χ2 = 52.66, df = 14 (P < 0.001); I2 = 61.3%
Strong
Anxiety
4,19–21,23,41,50,73,83,84,90–92,96,117,118
16
3295
0.60 (0.56 to 0.63)
χ2 = 58.35, df = 15 (P < 0.001); I2 = 72%
Strong
Pain catastrophizing
4,12,18,28,37,48,54,56,58,84,92,96,116–118,134,136
17
3812
0.48 (0.43 to 0.54)
χ2 = 82.56, df = 16 (P < 0.001); I2 = 82.6%
Moderate–strong
Stress
4,23,41,132,138
5
1889
0.62 (0.56 to 0.69)
χ2 = 27.79, df = 4 (P < 0.001); I2 = 80.2%
Strong
Sleep
4,54,90,91
4
2649
0.40 (0.29 to 0.51)
χ2 = 44.57, df = 3 (P < 0.001); I2 = 92.8%
Moderate
Kinesiophobia
4,85,118,132,134
5
1865
0.46 (0.40 to 0.53)
χ2 = 16.08, df = 4 (P = 0.003); I2 = 80%
Moderate–strong
Pressure pain threshold
23,28,35,37,41,58,82,86,96,98,138
14
1272
−0.22 (−0.28 to −0.17)
χ2 = 17.37, df = 13 (P = 0.183); I2 = 24.2%
Weak
Heat pain threshold
23,96
3
132
−0.01 (−0.12 to 0.09)
χ2 = 1.16, df = 2 (P = 0.558); I2 = 0%
No effect
Conditioned pain modulation
37,58,86,98,138
6
679
0.10 (0.01 to 0.19)
χ2 = 9.09, df = 5 (P = 0.106); I2 = 45.5%
Weak
Temporal summation
37,41,82,86,96
7
600
0.09 (0.01 to 0.16)
χ2 = 6.07, df = 6 (P = 0.415); I2 = 0%
None–weak (negligible)
Widespread pain
37,82,96,136
6
455
0.39 (0.23 to 0.50)
χ2 = 10.53, df = 5 (P = 0.061); I2 = 54.9%
Moderate
All participants—PSQ
Depression
23,33,38,42,46,57,61,61,78,99,103,106,107,112
17
2985
0.11 (0.08 to 0.15)
χ2 = 16.71, df = 17 (P = 0.474); I2 = 0%
Weak
Anxiety
23,33,38,42,45,49,60,61,78,99,103,106,107,142,145
18
2859
0.16 (0.14 to 0.19)
χ2 = 9.65, df = 17 (P = 0.918); I2 = 0%
Weak
Pain catastrophising
7,9,33,45,46,52,60,61,78,99,107,112,142
15
1980
0.32 (0.25 to 0.40)
χ2 = 48.97, df = 14 (P < 0.001); I2 = 82.6%
Moderate
Stress
17,23,60
3
261
0.23 (0.15 to 0.30)
χ2 = 1.39, df = 2 (P = 0.49); I2 = 0%
Weak
Pressure pain threshold
23,33,38,42,60,106,108,109,129
10
2464
−0.17 (−0.22 to −0.12)
χ2 = 13.94, df = 9 (P = 0.124); I2 = 35.6%
Weak
Heat pain threshold
23,33,38,60,106,108,128
9
1078
−0.11 (−0.25 to 0.02)
χ2 = 46.77, df = 8 (P < 0.001); I2 = 84.8%
Weak
Conditioned pain modulation
60,61,103
4
508
−0.04 (−0.15 to 0.07)
χ2 = 7.00, df = 3 (P = 0.072); I2 = 57.2%
No effect
Temporal summation
33,60,61
4
865
0.08 (−0.00 to 0.17)
χ2 = 6.36, df = 3 (P = 0.095); I2 = 53.6%
None–weak (negligible)
Phasic heat
106–108
3
158
0.64 (0.54 to 0.73)
χ2 = 3.03, df = 2 (P = 0.219); I2 = 31.9%
Strong
Tonic cold
106–108
3
158
0.64 (0.51 to 0.77)
χ2 = 6.1, df = 2 (P = 0.047); I2 = 63%
Strong
Pinprick stimulation
106–108
3
158
0.39 (0.38 to 0.40)
χ2 = 0.02, df = 2 (P = 0.991); I2 = 0%
Moderate
95% CI, 95% confidence interval; CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
Table 2 -
Comparison of healthy controls and chronic pain patients (
Pain Sensitivity Questionnaire ).
Outcome measures
Population
Studies
No. of populations
Total number of participants
Weighted mean correlation (95% CI)
Heterogeneity
Strength of correlation
Depression
Chronic pain population
23,33,38,46,61,61,103,106
8
1076
0.12 (0.06 to 0.18)
χ2 = 8.21, df = 7 (P = 0.314); I2 = 14%
Weak
Healthy participants
33,38,46,61,78,99,107,112
8
809
0.05 (−0.02 to 0.13)
χ2 = 10.15, df = 7 (P = 0.180); I2 = 31.8%
No effect
Anxiety
Chronic pain population
23,33,39,61,61,103,106
7
940
0.15 (0.10 to 0.19)
χ2 = 4.31, df = 6 (P = 0.634); I2 = 0%
Weak
Healthy participants
33,38,45,61,78,99,107,142
8
633
0.13 (0.06 to 0.19)
χ2 = 5.48, df = 7 (P = 0.601); I2 = 0%
Weak
Pain catastrophizing
Chronic pain population
7,33,52,60,61
7
929
0.36 (0.23 to 0.49)
χ2 = 37.35, df = 6 (P < 0.001); I2 = 90.7%
Moderate
Healthy participants
9,33,45,46,61,78,99,107,112,142
10
1097
0.25 (0.19 to 0.31)
χ2 = 11.57, df = 9 (P = 0.239); I2 = 27.4%
Weak–moderate
PPT
Chronic pain population
23,33,38,60,106,108
6
688
−0.18 (−0.26 to −0.09)
χ2 = 8.53, df = 5 (P = 0.129); I2 = 42%
Weak
Healthy participants
33,38,129
3
429
−0.07 (−0.10 to −0.03)
χ2 = 0.44, df = 2 (P = 0.803); I2 = 0%
None–weak (negligible)
HPT
Chronic pain population
23,33,38,61,106,108
6
688
−0.17 (−0.34 to 0.00)
χ2 = 33.55, df = 5 (P < 0.001); I2 = 86.7%
Weak
Healthy participants
33,38,78,128
4
279
−0.08 (−0.22 to 0.05)
χ2 = 5.41, df = 3 (P = 0.144); I2 = 44.8%
None–weak (negligible)
CPM
Chronic pain population
60,61,103
3
428
−0.04 (−0.22 to 0.04)
χ2 = 7.01, df = 2 (P = 0.03); I2 = 71.5%
No effect
Healthy participants
61
1
80
r = −0.04
No effect
TS
Chronic pain population
33,60,61
3
562
0.07 (0.02 to 0.14)
χ2 = 1.71, df = 2 (P = 0.42); I2 = 0%
None–weak (negligible)
Healthy participants
33,61
2
192
0.10 (−0.13 to 0.33)
χ2 = 5.29, df = 1 (P = 0.022); I2 = 81.3%
Weak
95% CI, 95% confidence interval; CPM, conditioned pain modulation; HPT, heat pain threshold; PPT, pressure pain threshold; TS, temporal summation.
Figure 4.: Forest plots to show meta-analytic correlations for the CSI or PSQ with outcome measures. (A) Forest plot to show meta-analytic correlation between depression scores and CSI total scores. (B) Forest plot to show meta-analytic correlation between anxiety scores and CSI total scores. (C) Forest plot to show meta-analytic correlation between pressure pain threshold scores and CSI total scores. (D) Forest plot to show meta-analytic correlation between temporal summation scores and CSI total scores. (E) Forest plot to show meta-analytic correlation between depression scores and PSQ total scores. (F) Forest plot to show meta-analytic correlation between anxiety scores and PSQ total scores. (G) Forest plot to show meta-analytic correlation between pressure pain threshold scores and PSQ total scores. (H) Forest plot to show meta-analytic correlation between temporal summation scores and PSQ total scores. CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
Figure 4-A.: Forest plots to show meta-analytic correlations for the CSI or PSQ with outcome measures. (A) Forest plot to show meta-analytic correlation between depression scores and CSI total scores. (B) Forest plot to show meta-analytic correlation between anxiety scores and CSI total scores. (C) Forest plot to show meta-analytic correlation between pressure pain threshold scores and CSI total scores. (D) Forest plot to show meta-analytic correlation between temporal summation scores and CSI total scores. (E) Forest plot to show meta-analytic correlation between depression scores and PSQ total scores. (F) Forest plot to show meta-analytic correlation between anxiety scores and PSQ total scores. (G) Forest plot to show meta-analytic correlation between pressure pain threshold scores and PSQ total scores. (H) Forest plot to show meta-analytic correlation between temporal summation scores and PSQ total scores. CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
Figure 4-B.: Forest plots to show meta-analytic correlations for the CSI or PSQ with outcome measures. (A) Forest plot to show meta-analytic correlation between depression scores and CSI total scores. (B) Forest plot to show meta-analytic correlation between anxiety scores and CSI total scores. (C) Forest plot to show meta-analytic correlation between pressure pain threshold scores and CSI total scores. (D) Forest plot to show meta-analytic correlation between temporal summation scores and CSI total scores. (E) Forest plot to show meta-analytic correlation between depression scores and PSQ total scores. (F) Forest plot to show meta-analytic correlation between anxiety scores and PSQ total scores. (G) Forest plot to show meta-analytic correlation between pressure pain threshold scores and PSQ total scores. (H) Forest plot to show meta-analytic correlation between temporal summation scores and PSQ total scores. CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
Figure 4-C.: Forest plots to show meta-analytic correlations for the CSI or PSQ with outcome measures. (A) Forest plot to show meta-analytic correlation between depression scores and CSI total scores. (B) Forest plot to show meta-analytic correlation between anxiety scores and CSI total scores. (C) Forest plot to show meta-analytic correlation between pressure pain threshold scores and CSI total scores. (D) Forest plot to show meta-analytic correlation between temporal summation scores and CSI total scores. (E) Forest plot to show meta-analytic correlation between depression scores and PSQ total scores. (F) Forest plot to show meta-analytic correlation between anxiety scores and PSQ total scores. (G) Forest plot to show meta-analytic correlation between pressure pain threshold scores and PSQ total scores. (H) Forest plot to show meta-analytic correlation between temporal summation scores and PSQ total scores. CSI, Central Sensitization InventoryPain Sensitivity Questionnaire .
3.6. Central Sensitization Inventory
When Cohen standards for effect sizes are applied,22 r = 0.58, 95% CI [0.54-0.62]), anxiety (r = 0.60, 95% CI [0.56-0.63]), pain catastrophizing (r = 0.48, 95% CI [0.43-0.54]), stress (r = 0.63, 95% CI [0.56-0.69]), sleep (r = 0.40, 95% CI [0.29-0.51]), and kinesiophobia (r = 0.46, 95% CI [0.40-0.53]) all showed moderate–strong weighted correlations with the CSI. There were not sufficient data for a meta-analysis on childhood trauma, somatization, and personality type (sensory profile and Brief Resilience Scale) and negative affect, but they all showed moderate–strong correlations with CSI.20,21,23,41,50,73,77 https://links.lww.com/PAIN/B753 Figs. 4A and B ).
Central sensitization inventoryr = −0.22, 95% CI [−0.28 to −0.17]), no correlation with HPT (r = −0.01, 95% CI [−0.12 to 0.09]), and a weak or negligible correlation with CPM (r = 0.10, 95% CI [0.01-0.19]) and TS (r = 0.09, 95% CI [0.01-0.16]). This suggests that CSI scores are a weak or negligible predictor of how an individual might respond to nociceptive sensory testing (see Supplementary Table 1B, available at https://links.lww.com/PAIN/B753 Figs. 4C and D ). The correlations between the CSI and sensory measures PPT, HPT, CPM, and TS are substantially weaker than their correlations with psychological constructs. Widespread pain was moderately correlated with the CSI (r = 0.39, 95% CI [0.23-0.50]). Area of pain, number of pain sites, and manual tender point count were also moderately correlated with the CSI (r = 0.46, P < 0.01; r = 0.45, P < 0.01; r = 0.43, P < 0.01; respectively).
3.7. Pain sensitivity questionnaire
The PSQ showed weak correlations with psychological questionnaires, depression (r = 0.11, 95% CI [0.08-0.15]), anxiety (r = 0.16, 95% CI [0.14-0.19]), and stress (r = 0.23, 95% CI [0.15-0.30]), and a moderate correlation with pain catastrophising (r = 0.32, 95% CI [0.25-0.40]) (see Supplementary Table 1c, available at https://links.lww.com/PAIN/B753 Figs. 4E and F ). Regarding measures that did not provide sufficient data for meta-analyses —the Brief Resilience Scale (BRS) showed a moderate negative correlation with PSQ in one study.23 8,9,23,36
The PSQ showed a weak negative correlation with PPT (r = −0.17, 95% CI [−0.22 to −0.12]) and HPT (r = −0.11, 95% CI [−0.25 to 0.02]), a negligible correlation with TS (r = 0.08, 95% CI [−0.00 to 0.17]), and no correlation with CPM (r = −0.04, 95% CI [−0.15 to 0.07]) (see Supplementary Table 1d, available at https://links.lww.com/PAIN/B753 Figs. 4G and H ). The PSQ was, however, strongly correlated with Phasic heat (r = 0.64, 95% CI [0.54-0.73]) and tonic cold (r = 0.64, 95% CI [0.51-0.77]) and moderately correlated with pinprick stimulation (r = 0.39, 95% CI [0.38-0.40]). This suggests that the PSQ does assess what it is designed for and can predict how people might respond to painful stimulations.
3.8. Comparison of the Pain Sensitivity Questionnaire and Central Sensitization Inventory
The correlations between the CSI and depression (Z = 23.10, P < 0.001), anxiety (Z = 20.74, P < 0.001), pain catastrophizing (Z = 6.90, P < 0.001), and stress (Z = 7.64, P < 0.001) were significantly stronger than their correlations with the PSQ. There was no significant difference between the CSI and PSQ correlations for nociceptive sensory measures: PPT (Z = −1.50, P = 0.066), HPT (Z = 1.08, P = 0.141), and TS (Z = 0.19, P = 0.425). Conditioned pain modulation showed weak or no correlations with both the CSI (r = 0.10, 95% CI [0.01-0.19]) and PSQ (r = −0.04, 95% CI [−0.15 to 0.07]).
3.9. Moderator analyses
For meta-analyses where high heterogeneity was found43 2 was above 50%), a moderator analysis was performed to assess whether correlations were influenced by the varied populations in each study. A weight-least squares regression was performed to assess if the correlations were influenced by age, sex, and type of assessment used (eg, different variations of depression questionnaires) and whether the data were published or retrieved by email and type of population (chronic pain patients or healthy participants). A weighted least-squares regression analysis was used because this method tends to be the most accurate method for identifying moderator variables.124 P = 0.036) and publication or retrieval of data (F = 27.62, P = 0.034). The retrieved data (weighted mean = 0.24, SD = 0.04) had a substantially lower mean compared with the published data (weighted mean = 0.47, SD = 0.02). Correlations between CSI and anxiety (F = 5.59, P = 0.032) and depression (F = 4.45, P = 0.053) were moderated by the type of population (chronic pain or healthy participants). The correlation between the PSQ and pressure pain threshold (F = 4.94, P = 0.053) was also moderated by the type of population. The PSQ correlation with CPM was moderated by age (F = 65.87, B = −0.985, P = 0.015). Conditioned pain modulation, TS, and tonic cold correlations with the PSQ may have also been subjected to high heterogeneity43 44 44
3.10. Healthy controls vs pain patients
There were insufficient data to run meta-analyses to compare healthy controls against patients with chronic pain for the CSI. Only 2 studies involving healthy participants correlated CSI with psychological constructs, and anxiety was the only measure taken by both studies83,96 r = 0.43, P < 0.01 and r = 0.14, P = 0.50, respectively), with the latter study having a small sample size (n = 26). The 2 lowest correlations for anxiety across all studies were for these 2 with healthy populations. Similarly, Midenfjored et al.83 r = 0.32, P < 0.01) which is considerably lower than the meta-analytic average (r = 0.58, 95% CI [0.54-0.62]). This suggests that correlations for the CSI with psychological questionnaires may be stronger in patients with pain compared with healthy participants although because of the low number of studies along with their small sample sizes this is inconclusive. More studies involving healthy participants are required to confirm this finding. The small sample sizes for healthy controls reporting lower correlations could help account for some of the high heterogeneity in our findings.44
Only 3 studies96,98,119 r = −0.33, P = 0.09; and r = −0.06, P = 0.60, respectively),96,98
There were sufficient data to run meta-analyses comparing patients with chronic pain vs healthy participants for the PSQ correlations with several of our primary outcomes. The meta-analyses for these comparisons can be seen in Table 2 .
Overall, correlations between the PSQ and psychological and sensory measures were stronger for patients with chronic pain compared with healthy participants, although not always significantly so. The only measure where the healthy participants showed a stronger correlation was TS; however, these correlations were not significantly different (Z = −0.36, P = 0.360). Pain catastrophizing (Z = 2.72, P = 0.003) and PPT (Z = −1.81, P = 0.035) showed significantly stronger correlations for patients with chronic pain than healthy controls. Depression (Z = 1.51, P = 0.065), anxiety (Z = 0.40, P = 0.346), HPT (Z = −1.28, P = 0.100), and CPM (Z = −0.42, P = 0.338) did not show significantly different correlations between chronic pain and healthy subject populations.
Regarding heterogeneity, with the exception of TS which only had 2 studies that assessed healthy controls and was, therefore, vulnerable to high heterogeneity because of a small number of studies being used in the meta-analyses ,44 2 = 50%).43
4. Discussion
We found that the CSI was strongly correlated with psychological measures (anxiety, depression, pain catastrophising, stress, sleep, and kinesiophobia). By contrast, it was not or only weakly correlated with QST measures; pain thresholds, TS, and CPM were all found to be low in their correlation to the CSI (all r 's < 0.3). Only the extent of widespread pain as a sensory measure reached a moderate correlation with the CSI, which differs in methodology from the other “sensory” measures as it does not involve experimental laboratory testing of nociception. Compared with the CSI, the PSQ showed significantly lower correlation coefficients with psychological constructs. For their correlations with sensory measures (QST), there was no significant difference between the 2 questionnaires. The PSQ was, however, associated with measures of pain where participants were asked to rate a set of painful stimulus, phasic heat, tonic cold, and pinprick stimulation. Therefore, there is evidence to suggest that PSQ does measure what it was designed for, ie, sensitivity to pain. However, there is little evidence from QST correlations to suggest either questionnaire is a useful tool for identifying enhanced nociceptive sensitivity or altered pain modulation. These findings concur with a previous small empirical report.23
There is a lack of data that assesses the association between the CSI and human surrogate models of CS (measures specifically designed in an attempt to quantify the “increased responsiveness of nociceptive neurons in the central nervous system to either normal or subthreshold afferent input”).47 100 141 137 central sensitization .5,121,127,135 Quantitative sensory testing is a largely standardised method used to evaluate sensory profiles in response to stimuli, assessing the functional integrity of small and large nerve fibre afferents and central somatosensory pathways.25,53,131 5,123 69,123 r = 0.09) which is likely to be of low clinical relevance. The PSQ shows a similarly weak correlation with TS (r = −0.08). Low correlations between CPM and the 2 questionnaires were found. Conditioned pain modulation is said to quantify the efficiency of endogenous inhibition of pain based on the concept of “pain inhibits pain” derived from diffuse noxious inhibitory control in animal studies.27,66,93
The CSI is a self-report questionnaire designed to identify patients who might have a CSS, for which central sensitization is believed to be an aetiological cause, such as fibromyalgia, temporomandibular disorder, or migraine or tension headaches.88,89 74 89 central sensitization . Neblett et al.89 47
The CSI has strong correlations with other validated self-report measures of psychological constructs (eg, anxiety, depression, stress, etc), which has previously been given as evidence for convergent validity.88 115 central sensitization ” from its initial use in preclinical studies to the term used in this inventory. It may be that CS and emotional sensitivity are closely linked,26,30 systematic review demonstrates, the CSI loads almost exclusively on emotional sensitivity rather than on measures of nociceptive response. It appears that a form of construct drift regarding the definition of CS has occurred across disciplines where patients in clinics have been assumed to be suffering from CS based on their psychological difficulties that occur alongside pain. One practical reason for this drift is that it allows a clinician to finally diagnose a “medically unexplained” condition in a patient in a way that suggests a known physiological mechanism. From the patient's perspective, this is comforting and may legitimize their pain. This review raises questions, however, about the degree to which such a diagnosis actually indicates a known physiological mechanism.
Owing to the large scope of the study, one limitation is that only single extraction was performed, with extracted correlations being checked by a second author. One interesting observation and another possible limitation is the lack of data that assesses the CSI with electrical and thermal stimuli. Similarly, few studies explore the relationship between CSI and psychological or nociceptive sensitivity in healthy participants. This suggests that the questionnaire is largely used in clinical populations with only certain mechanical instruments available to assess CS. More research is required in laboratory settings that can further assess the association between the CSI and CS. This systematic review was largely restricted to QST, with no studies found assessing the CSI or PSQ against measured areas of allodynia or hyperalgesia. Future research should include the CSI with human surrogate models of CS using thermal, electrical, and capsaicin models that induce measurable areas of hyperalgesia or allodynia100
To conclude, there is not strong evidence in the literature to suggest that self-report instruments currently available (the CSI or PSQ) reflect CS. The CSI seems to identify people with a psychological vulnerability that is associated with pain, rather than CS itself. It may be valuable in identifying a hypervigilant state that is common in many patients with chronic pain. However, on its own, we question its utility as a tool for identifying CS as defined by an increased responsiveness of nociceptive neurons.64 106–108
Conflict of interest statement
The authors have no conflict of interest to declare.
Appendix A. Supplemental digital content
Supplemental digital content associated with this article can be found online at https://links.lww.com/PAIN/B753
Supplemental video content
A video abstract associated with this article can be found at https://links.lww.com/PAIN/B754
Acknowledgements
Work on this article was funded by a New Investigator Research Grant to TVS from the Medical Research Council, UK (MR/R005656/1).
Author Contributions: G. R. Adams, R. Harrison, W. Gandhi, C. M. van Reekum, D. Wood-Anderson, I. Gilron, and T. V. Salomons helped prepare and write the protocol. D. Wood-Anderson helped with data checks.
PROSPERO registration: (CRD42021208731).
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