Do “central sensitization” questionnaires reflect measures of nociceptive sensitization or psychological constructs? A systematic review and meta-analyses : PAIN

Journal Logo

Systematic Review and Meta-Analysis

Do “central sensitization” questionnaires reflect measures of nociceptive sensitization or psychological constructs? A systematic review and meta-analyses

Adams, Greig R.a; Gandhi, Wiebkea; Harrison, Richarda; van Reekum, Carien M.a; Wood-Anderson, Desmondb; Gilron, Ianc; Salomons, Tim V.a,b,*

Author Information
PAIN 164(6):p 1222-1239, June 2023. | DOI: 10.1097/j.pain.0000000000002830


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 with “central sensitivity syndrome (CSS)” being a recently developed diagnosis for several “medically unexplained” pain disorders (eg, fibromyalgia and temporomandibular disorder) for which CS is believed to play an etiological role.143 As of yet, there is no conclusive method of accurately establishing the presence of CS in humans,100 although 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), and conditioned pain modulation (said to quantify the efficiency of endogenous inhibition of pain).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 Self-report questionnaires would make a pragmatic alternative assessment of CS in clinics, allowing for quick and convenient assessment at little cost. To serve this purpose, however, these questionnaires would need to demonstrate acceptable associations with known measures of CS to show sufficient construct validity.

A self-report questionnaire that is widely used in the assessment of central sensitization is the Central sensitization Inventory (CSI). The CSI was designed as a self-report screening measure to help identify patients who might have a CSS, such as fibromyalgia, neck injury, temporomandibular joint disorder, or migraine or tension headaches.89 It has been shown to be a reliable and valid psychometric instrument for identifying individuals vulnerable to pain.74

The Pain Sensitivity Questionnaire (PSQ), however, may more directly measure the sensory facilitation involved in CS.107 The PSQ is a valid and reliable measured designed to investigate self-reported pain sensitivity as a supplement or alternative to experimental pain testing.80,108 It focuses more on respondents imagining situations that involve nociceptive input and predicting how they would react. The questions are posed to measure sensitization to sensory input, but the degree to which it reflects a top–down component influenced by the personality type or disposition remains open. More specifically, whether it reflects these psychological profiles to the same degree as the CSI is a germane question for understanding how closely related and psychometrically distinct these measures are.

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 to a broader usage including psychological profiles. This construct drift may reflect a noble desire to legitimize poorly explained pain by providing a putative central mechanism, but the degree to which this broader construct reflects mechanisms outlined in animal models of 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 and registered in the International Prospective Register of Systematic Reviews (PROSPERO) database ( prepared in accordance with recommendations specified in the Preferred Reporting Items for Systematic Reviews and 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 inventory” or “central sensitization inventory” or “pain 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 inventory” or “central sensitization inventory” or “pain 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 Inventory; PSQ, Pain 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 the 4-dimensional Symptom Questionnaire (4DSQ),125 the Hospital Anxiety and Depression Scale (HADS-D),146 the Symptom Checklist 90 Depression Subscale (SCL90-D),14 Beck Depression Inventory (BDI),114 the Depression Scale (DEPS),97 the Centre for Epidemiologic Studies Depression Scale (CES-D), the Geriatric Depression Scale (GDS-15),101 and the Hamilton Depression Rating Scale (HAM-D).71 The questionnaires used to assess anxiety were DASS21,67 4DSQ,125 HADS-A,146 the Symptom Checklist 90 anxiety subscale (SCL90-A),14 the State and Trait Anxiety Inventory (STAI),81 the Brief Symptom Inventory (BSI),34 the Beck Anxiety Inventory (BAI), and the Hamilton Anxiety Rating Scale (HAM-A).71 The questionnaire used to assess pain catastrophising was the Pain Catastrophising Scale (PCS).94 The questionnaires used to assess stress were the DASS-21,67 the Perceived Stress Scale (PSS),68 4DSQ125and the Brief Measure of Emotional Preoperative Stress (B-MEPS).138 The questionnaires used to assess sleep were the Medical Outcomes Study Sleep Scale (MOS),3 the Basic Scale of Insomnia Complaints and Quality of Sleep (BaSIQs),2 and the Insomnia Severity Index (ISI).87 The questionnaire used to assess kinesiophobia was the Tampa Scale of Kinesiophobia (TSK).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 the Patient Health Questionnaire (PHQ-15),59 the Adolescent or Adult Sensory Profile (AASP),15 the Sensory Responsiveness Questionnaire (SRQ),10 the Difficulties in Emotion Regulation Scale (DERS),13 the Positive and Negative Affect Schedule (PANAS),24 the Pain Anxiety Sensitivity Scale (PASS),75 the Childhood Trauma Questionnaire (CTQ),11 and the Child Abuse and Trauma questionnaire (CATS).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 offset analgesia,119 pain tolerance33,55,60,99,128,129,138 (electrical and pressure or thermal), area or number of pain sites,70,86 cold pain threshold,38 electrical pain threshold,103 and tonic heat.107 Correlations for the CSI or PSQ with these measures can be found in Supplementary Tables 1a-d (available at

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 and another used a hot water bath.103 Most commonly, the test stimulus was a predetermined pressure applied to a body part on the opposite side of the conditioning stimulus using a pressure algometer. One study used a combination of pressure and heat pain,60 and another used electrical stimuli applied to the right sural nerve.103 One study used PPT as the test stimulus and ischemic compression using a sphygmomanometer was used as the conditioning stimulus.98 The average pain ratings for the test stimulus given during the conditioning stimulus condition (both test and conditioning stimulus simultaneously administered) were subtracted from the average pain ratings of the test-stimulus only condition to give a CPM score.

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 Another study applied a 60 g von Frey filament to the forearm and knee for 4 stimulations; the stimulus was then applied for 30 stimulations at a rate of 1/second. The initial pain rating was subtracted from the second to give a TS score.37

2.8.7. Widespread Pain

Widespread pain measures consisted of the Widespread Pain Index (WPI)32 and the Michigan Body Map (MBM),16 which are both questionnaires used to assess the number of painful body sites and area of pain across the body.

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 was conducted by 2 independent authors (G.R.A. and W.G./R.H.) to evaluate potential sources of bias across 5 domains: participation bias, publication bias, attrition, methodological quality, and statistical analysis. We assessed the following for each study: (1) Were potential sources of participation bias considered and addressed? (2) Was there any missing data regarding the variables of interest? (3) Was the methodology of the variable of interest of a standardized quality? (4) Was the desirable statistical analyses performed? (5) Was the sample size adequate? Each category was assigned a low, unclear, or high risk of bias and presented with a “risk of bias” summary. Disagreements between reviewers were resolved by discussion and consensus. A third reviewer (W.G./R.H.) was consulted where assessments could not be agreed on.

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 Inventory correlations were extracted for each construct separately: depression, anxiety, stress, pain catastrophising, kinesiophobia, and sleep. Meta-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 to show individual study characteristics. This table was presented in 4 sections: CSI with psychological constructs (Supplementary Table 1a, available at, CSI with nociceptive measures (Supplementary Table 1b, available at, PSQ with psychological constructs (Supplementary Table 1c, available at, and PSQ with nociceptive measures (Supplementary Table 1d, available at 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 Any other findings with insufficient data for a meta-analysis were reviewed narratively where appropriate.

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 Statistical software StatsDirect version 3.3.5 was used to calculate weighted mean correlations, to test for heterogeneity and to perform subgroup analysis.122 Weighted mean correlations were calculated to measure the overall strength of a correlation between the PSQ or CSI and each construct. A subgroup analysis was performed where applicable to assess and compare the relative strength of our correlations of interest for patients with chronic pain vs healthy controls.

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 A value of 0% implies no observed heterogeneity, 1% to 40% indicates low heterogeneity, 30% to 60% suggests moderate heterogeneity, 50% to 90% signifies substantial heterogeneity, and 75% to 100% is considered heterogeneous.43 Moderator analyses were conducted when heterogeneity scores were above 50% to examine whether certain factors contributed towards heterogenous findings (eg, age or sex).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 Similarly, owing to the typical nature of chronic pain populations, some study populations were predominantly female, and the mean age was high (over 60).

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 However, this study was still included for further analysis as the potentially affected representation of females was balanced against having an international sample.

3.2. Excluded studies

Two studies were excluded due to a lack of methodological standardization,76,133 and one study gave partial correlations and was, therefore, excluded.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 Inventory across 37 different studies and 5531 took the Pain Sensitivity Questionnaire across 30 different studies (one study included both questionnaires).23 Thirty-one studies containing a total of 6885 participants provided correlations for the CSI with at least one psychological primary outcome. Sixteen studies consisting of 1857 participants provided correlations for the CSI with at least one sensory primary outcome. Twenty-five studies consisting of a total of 4482 participants provided correlations for the PSQ with at least one psychological primary outcome. Sixteen studies consisting of a total of 3375 participants provided correlations for the PSQ with at least one primary sensory measure. All extracted correlations can be seen in Supplementary Tables 1a-d (available at

Supplementary Tables 1a-d (available at show the extracted data for each of these studies (Supplementary Table 1a, available at shows all extracted correlations for CSI with psychological primary outcomes, Supplementary Table 1b, available at for CSI with sensory primary outcomes, Supplementary Table 1c, available at for PSQ with psychological primary outcomes, and Supplementary Table 1d, available at for PSQ with sensory primary outcomes). All tables provide data for age, sex, and subject population for each study if it was provided. Note that if multiple correlations of the same or very similar constructs were provided for a study, which only occurs when trait anxiety and state anxiety are reported within the STAI, the values were averaged together as these 2 measures have high intercorrelation.102 Similarly, if measures for multiple body sites were recorded for PPT, HPT, CPM etc., the multiple correlations per study were averaged together to give a mean correlation score for that construct, given that intraclass correlations for pain measures across different body sites have been found to be high (Cronbach alpha values >0.80).63,72 This meant that one correlation score for each construct was provided for each population which could be used for meta-analysis.

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

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 Inventory; PSQ, Pain 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 Inventory; PSQ, Pain 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 Inventory; PSQ, Pain 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 Inventory; PSQ, Pain 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 Inventory; PSQ, Pain Sensitivity Questionnaire.

3.6. Central Sensitization Inventory

When Cohen standards for effect sizes are applied,22 the CSI showed moderate-to-strong correlations with psychological questionnaires. Depression (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 Overall, these findings suggest that the CSI correlates strongly with psychological factors (see Supplementary Table 1A, available at and Figs. 4A and B).

Central sensitization inventory shows a weak negative correlation with PPT (r = −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 and 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 and 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 Negative affect neuroticism aversiveness, past negativity, and sensory responsiveness showed weak–moderate positive correlations with the PSQ.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 and 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 (ie, I2 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 The correlation between sleep and CSI (our highest score of heterogeneity at 92.8%) was moderated by sex (F = 26.22, B = 0.964, 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 because of the small number of studies incorporated in the meta-analysis.44 The heterogeneity for CSI with stress, sleep, and kinesiophobia may also be inflated because of the small number of studies being included for meta-analysis.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 reported a correlation for CSI with depression (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 correlated the CSI with nociceptive sensory correlates for healthy participants. Of these, PPT was the only sensory measure recorded in more than one study (r = −0.33, P = 0.09; and r = −0.06, P = 0.60, respectively),96,98 with both of these studies having a small sample size (n = 28 and n = 31, respectively). There were insufficient data to make any clear conclusions about whether healthy participants and pain populations differed in their correlations for CSI and nociceptive sensory measures (QST).

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 heterogeneity was below the suggested benchmark for substantial heterogeneity in healthy controls (I2 = 50%).43 The overall higher heterogeneity found in the chronic pain group suggests that the type of pain may be an important factor that moderates our correlations. To conclude, high heterogeneity in some of our findings appears to be down to different target populations being used across studies with characteristics such as age, sex, and type of pain having a moderating influence on correlations.

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 Quesada et al.100 provided a review and practical guide of different models of CS in human findings that there were more than a dozen methods used to induce hyperalgesia and allodynia (characteristics of CS), including intradermal or topical capsaicin, low-frequency or high-frequency electrical stimulation, and thermode induced heat injury. Duration and area of hypersensitivity were subsequently measured to examine the presence of CS. We found none of these measures of CS to be assessed alongside the CSI or PSQ in this review. More studies should be conducted to directly examine correlations between the CSI and these measures of CS. Although we acknowledge that QST measures are not direct measures of CS, for this review, they were the best measures available for identifying pain facilitatory mechanisms of a pronociceptive phenotype that are associated with chronic pain,141 which might suggest the presence of CS.137 Several studies, for instance, use QST measures to indicate central sensitization.5,121,127,135 As a result, this review is limited to QST measures (TS, CPM, and pain thresholds) as the primary form of physical assessment for CS. 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 One of these measures that is particularly considered an indication of CS is TS.5,123 Temporal summation assesses the windup that occurs from repetitive stimulation of peripheral C fibres and is believed to reflect summation mechanisms of dorsal horn neurons.69,123 We would, therefore, expect self-report assessments related to CS (such as CSI) to be strongly correlated with TS. However, this review has revealed that the CSI and TS show only a very weak correlation (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 There is no evidence to suggest either of these questionnaires could be used to help identify underlying mechanisms of pain facilitation or inhibition in humans.

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 The CSI consists of 2 parts. Part A includes 25 questions related to common CSS symptoms such as fatigue, depression, poor sleep, lack of concentration, and general sensitivity. Part B determines if the patient has been diagnosed with certain CSS disorders or related disorders, such as chronic fatigue syndrome, migraines, fibromyalgia, etc.74 A 2015 study by Neblett et al.89 found the CSI accurately aligned with a CSS diagnosis in 82.8% of participants, whereas 54.8% of participants were correctly identified as not having a CSS diagnosis. It appears that the CSI can be useful in diagnosing CSS, but the diagnosis of CSS is not scientifically precise itself, particularly with respect to its relationship to central sensitization. Neblett et al.89 stated that a diagnosis of CSS relied on tender point evaluations being conducted on people suspected of having fibromyalgia and trigger point evaluations for patients suspected of having myofascial pain syndrome. There are, therefore, some assumptions made about the presence of CS in these cases to begin with, with tender and trigger point evaluations being used to confirm the presence of CS– these measures would not be sufficient to accurately identify an “increased responsivity of nociceptive neurons.” The CSI asks if the patient has been diagnosed with certain CSS disorders or shows associated symptoms; thus, intercorrelation between the 2 would be expected to be high. Although the CSI aligns with diagnosis of CSS, questions remain about whether either diagnostic accurately identifies “an increased responsiveness of nociceptive neurons.”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 A recent review, however, recommends that data using the CSI should be interpreted with caution as so far there is no scientific evidence to suggest that the CSI is a valid indicator of CS.115 No self-report questionnaire can identify the physiological mechanism of CS. It appears that there has been expansion of the term “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 but as this 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 and the assessment of both healthy participants and pain patients. There is clearly a need for more data to assess the relevance of the CSI related to CS and on what basis of research the questionnaire was formulated. If the CSI was a good tool for identifying CS, we would expect high scores on the CSI to be reflected in neuropathic pain patients who have clinical signs of CS. Strikingly, however, we found that patients with neuropathic pain are typically excluded from studies incorporating the CSI.

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 More research is required to develop an optimal method for identifying CS in humans, with these models being used to assess the construct validity of the CSI as an accurate indicator of CS. The PSQ does seem to correlate with experimental measures of pain sensitivity, not QST, albeit these measures are conducted by one particular laboratory group.106–108 More studies assessing the validity of the PSQ with experimental measures of nociception should be conducted by different laboratory groups. Overall, there is no evidence to suggest either self-report questionnaire is suitable for assessing CS in humans or identifying a pronociceptive phenotype, based on QST measures.

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

Supplemental video content

A video abstract associated with this article can be found at


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).


[1]. Adams GR, Gandhi W, Harrison R, van Reekum CM, Gilron I, Salomons TV. Do “central sensitization” questionnaires reflect measures of nociceptive sensitization or psychological constructs? Protocol for a systematic review. Pain Rep 2021;6:e962.
[2]. Allen Gomes A, Ruivo Marques D, Meia-Via AM, Meia-Via M, Tavares J, Fernandes da Silva C, Pinto de Azevedo MH. Basic Scale on Insomnia complaints and Quality of Sleep (BaSIQS): reliability, initial validity and normative scores in higher education students. Chronobiol Int 2015;32:428–40.
[3]. Allen RP, Kosinski M, Hill-Zabala CE, Calloway MO. Psychometric evaluation and tests of validity of the medical outcomes study 12-item sleep scale (MOS sleep). Sleep Med 2009;10:531–9.
[4]. Andias R, Silva AG. Cross-cultural adaptation and psychometric properties of the European Portuguese version of the central sensitization inventory in adolescents with musculoskeletal chronic pain. Pain Pract 2020;20:480–90.
[5]. Arendt-Nielsen L. Central sensitization in humans: assessment and pharmacology. Handb Exp Pharmacol 2015;227:79–102.
[6]. Arendt-Nielsen L, Yarnitsky D. Experimental and clinical applications of quantitative sensory testing applied to skin, muscles and viscera. J Pain 2009;10:556–72.
[7]. Azimi P, Azhari S, Shahzadi S, Nayeb Aghaei H, Mohammadi HR, Montazeri A. Outcome measure of pain in patients with lumbar disc herniation: validation study of the Iranian version of pain sensitivity questionnaire. Asian Spine J 2016;10:480–7.
[8]. Bar-Shalita T, Cermak SA. Multi-sensory responsiveness and personality traits predict daily pain sensitivity. Front Integr Neurosci 2019;13:77.
[9]. Bar-Shalita T, Deutsch L, Honigman L, Weissman-Fogel I. Ecological aspects of pain in sensory modulation disorder. Res Develop Disabil 2015;45–46:157–67.
[10]. Bar-Shalita T, Seltzer Z, Vatine J-J, Yochman A, Parush S. Development and psychometric properties of the sensory responsiveness questionnaire (SRQ). Disabil Rehabil 2009;31:189–201.
[11]. Bernstein DP, Ahluvalia T, Pogge D, Handelsman L. Validity of the childhood trauma questionnaire in an adolescent psychiatric population. J Am Acad Child Adolesc Psychiatry 1997;36:340–8.
[12]. Bilika P, Neblett R, Georgoudis G, Dimitriadis Z, Fandridis E, Strimpakos N, Kapreli E. Cross-cultural adaptation and psychometric properties of the Greek version of the central sensitization inventory. Pain Pract 2020;20:188–96.
[13]. Bjureberg J, Ljótsson B, Tull MT, Hedman E, Sahlin H, Lundh L-G, Bjärehed J, DiLillo D, Messman-Moore T, Gumpert CH, Gratz KL. Development and validation of a brief version of the difficulties in emotion regulation scale: the DERS-16. J Psychopathol Behav Assess 2016;38:284–96.
[14]. Bonicatto S, Dew MA, Soria JJ, Seghezzo ME. Validity and reliability of symptom checklist '90 (SCL90) in an Argentine population sample. Soc Psychiatry Psychiatr Epidemiol 1997;32:332–8.
[15]. Brown C, Tollefson N, Dunn W, Cromwell R, Filion D. The adult sensory profile: measuring patterns of sensory processing. Am J Occup Ther 2001;55:75–82.
[16]. Brummett CM, Bakshi RR, Goesling J, Leung D, Moser SE, Zollars JW, Williams DA, Clauw DJ, Hassett AL. Preliminary validation of the Michigan body Map. PAIN 2016;157:1205–12.
[17]. Cathcart S, Bhullar N, Immink M, Della Vedova C, Hayball J. Pain sensitivity mediates the relationship between stress and headache intensity in chronic tension-type headache. Pain Res Manag 2012;17:377–80.
[18]. Caumo W, Antunes LC, Lorenzzi Elkfury J, Herbstrith EG, Busanello Sipmann R, Souza A, Torres I, Souza dos Santos V, Neblett R. The Central Sensitization Inventory validated and adapted for a Brazilian population: psychometric properties and its relationship with brain-derived neurotrophic factor. J Pain Res 2017;10:2109–22.
[19]. Chiarotto A, Viti C, Sulli A, Cutolo M, Testa M, Piscitelli D. Cross-cultural adaptation and validity of the Italian version of the central sensitization inventory. Musculoskelet Sci Pract 2018;37:20–8.
[20]. Clark JR, Nijs J, Yeowell G, Holmes P, Goodwin PC. Trait sensitivity, anxiety, and personality are predictive of central sensitization symptoms in patients with chronic low back pain. Pain Pract 2019;19:800–10.
[21]. Clark JR, Yeowell G, Goodwin PC. Trait anxiety and sensory processing profile characteristics in patients with non-specific chronic low back pain and central sensitization—a pilot observational study. J Bodywork Mov Ther 2018;22:909–16.
[22]. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: L. Erlbaum Associates, 1988.
[23]. Coronado RA, George SZ. The Central Sensitization Inventory and Pain Sensitivity Questionnaire: an exploration of construct validity and associations with widespread pain sensitivity among individuals with shoulder pain. Musculoskelet Sci Pract 2018;36:61–7.
[24]. Crawford JR, Henry JD. The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample. Br J Clin Psychol 2004;43:245–65.
[25]. Cruz-Almeida Y, Fillingim RB. Can quantitative sensory testing move us closer to mechanism-based pain management? Pain Med 2014;15:61–72.
[26]. Curatolo M, Arendt-Nielsen L, Petersen-Felix S. Central hypersensitivity in chronic pain: mechanisms and clinical implications. Phys Med Rehabil Clin N Am 2006;17:287–302.
[27]. Damien J, Colloca L, Bellei-Rodriguez C-É, Marchand S. Pain modulation: from conditioned pain modulation to placebo and nocebo effects in experimental and clinical pain. Int Rev Neurobiol 2018;139:255–96.
[28]. De Groef AD, Meeus M, De Vrieze T, Vos L, Van Kampen M, Geraerts I, Devoogdt N. Unraveling self-reported signs of central sensitization in breast cancer survivors with upper limb pain: prevalence rate and contributing factors. Pain Physician 2018;21:E247–56.
[29]. Deeks JJ, Higgins JP, Altman DG. Analysing data and undertaking meta-analyses. Cochrane handbook for systematic reviews of interventions. 2019. p. 241–84. doi: 10.1002/9781119536604.ch10.
[30]. Diatchenko L, Nackley AG, Slade GD, Fillingim RB, Maixner W. Idiopathic pain disorders–pathways of vulnerability. PAIN 2006;123:226–30.
[31]. Dubin AE, Patapoutian A. Nociceptors: the sensors of the pain pathway. J Clin Invest 2010;120:3760–72.
[32]. Dudeney J, Law E, Meyyappan M, Palermo T, Rabbitts J. Evaluating the psychometric properties of the widespread pain index to assess pain extent in youth with painful conditions. J Pain 2018;19:S54.
[33]. Forstenpointner J, Ruscheweyh R, Attal N, Baron R, Bouhassira D, Enax-Krumova EK, Finnerup NB, Freynhagen R, Gierthmühlen J, Hansson P, Jensen TS, Maier C, Rice ASC, Segerdahl M, Tölle T, Treede R-D, Vollert J. No pain, still gain (of function): the relation between sensory profiles and the presence or absence of self-reported pain in a large multicenter cohort of patients with neuropathy. PAIN 2021;162:718–27.
[34]. Franke GH, Jaeger S, Glaesmer H, Barkmann C, Petrowski K, Braehler E. Psychometric analysis of the brief symptom inventory 18 (BSI-18) in a representative German sample. BMC Med Res Methodol 2017;17:14.
[35]. French HP, Jong CC, McCallan M. Do features of central sensitization exist in Greater Trochanteric Pain Syndrome (GTPS)? A case control study. Musculoskelet Sci Pract 2019;43:6–11.
[36]. Gacs B, Birkas B, Csatho A. Time perspectives and pain: negative time perspective profile predicts elevated vulnerability to pain. Pers Individ Differ 2020;153:109616.
[37]. Gervais-Hupe J, Pollice J, Sadi J, Carlesso LC. Validity of the central sensitization inventory with measures of sensitization in people with knee osteoarthritis. Clin Rheumatol 2018;37:3125–32.
[38]. Grundström H, Larsson B, Arendt-Nielsen L, Gerdle B, Kjolhede P. Associations between pain thresholds for heat, cold and pressure, and Pain Sensitivity Questionnaire scores in healthy women and in women with persistent pelvic pain. Eur J Pain 2019;23:1631–9.
[39]. Grundstrom H, Larsson B, Arendt-Nielsen L, Gerdle B, Kjolhede P. Pain catastrophizing is associated with pain thresholds for heat, cold and pressure in women with chronic pelvic pain. Scand J Pain 2020;20:635–46.
[40]. Hayden JA, Côté P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med 2006;144:427–37.
[41]. Hendriks E, Voogt L, Lenoir D, Coppieters I, Ickmans K. Convergent validity of the central sensitization inventory in chronic whiplash-associated disorders; associations with quantitative sensory testing, pain intensity, fatigue, and psychosocial factors. Pain Med 2020;21:3401–12.
[42]. Hermesdorf M, Berger K, Baune BT, Wellmann J, Ruscheweyh R, Wersching H. Pain sensitivity in patients with major depression: differential effect of pain sensitivity measures, somatic cofactors, and disease characteristics. J Pain 2016;17:606–16.
[43]. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–60.
[44]. von Hippel PT. The heterogeneity statistic I2 can be biased in small meta-analyses. BMC Med Res Methodol 2015;15:35.
[45]. Honigman L, Yarnitsky D, Sprecher E, Weissman-Fogel I. Psychophysical testing of spatial and temporal dimensions of endogenous analgesia: conditioned pain modulation and offset analgesia. Exp Brain Res 2013;228:493–501.
[46]. Huang A, Katz J, Clarke H. Ensuring safe prescribing of controlled substances for pain following surgery by developing a transitional pain service. Pain Manag 2015;5:97–105.
[47]. IASP terminology. IASP. Available at: Accessed August 2, 2019.
[48]. Imai R, Imaoka M, Nakao H, Hida M, Tazaki F, Omizu T, Ishigaki T, Nakamura M. Association between chronic pain and pre-frailty in Japanese community-dwelling older adults: a cross-sectional study. PLoS One 2020;15:e0236111.
[49]. Jakubczyk A, Wiśniewski P, Trucco EM, Kobyliński P, Zaorska J, Skrzeszewski J, Suszek H, Wojnar M, Kopera M. The synergistic effect between interoceptive accuracy and alcohol use disorder status on pain sensitivity. Addict Behav 2021;112:106607.
[50]. Johnson BN, Lumley MA, Cheavens JS, McKernan LC. Exploring the links among borderline personality disorder symptoms, trauma, and pain in patients with chronic pain disorders. J Psychosom Res 2020;135:110164.
[51]. Khera T, Rangasamy V. Cognition and pain: a review. Front Psychol 2021;12:12.
[52]. Kim H-J, Ruscheweyh R, Yeo J-H, Cho H-G, Yi J-M, Chang B-S, Lee C-K, Yeom JS. Translation, cross-cultural adaptation, and validity of the Korean version of the Pain Sensitivity Questionnaire in chronic pain patients. Pain Pract 2014;14:745–51.
[53]. Kim Y-K, Yun P-Y, Kim J-H, Lee J-Y, Lee W. The quantitative sensory testing is an efficient objective method for assessment of nerve injury. Maxillofac Plast Reconstr Surg 2015;37:13.
[54]. Knezevic A, Neblett R, Colovic P, Jeremic-Knezevic M, Bugarski-Ignjatovic V, Klasnja A, Pantelinac S, Pjevic M. Convergent and discriminant validity of the Serbian version of the central sensitization inventory. Pain Pract 2020;20:724–36.
[55]. Kohl A, Rief W, Glombiewski JA. Do fibromyalgia patients benefit from cognitive restructuring and acceptance? An experimental study. J Behav Ther Exp Psychiatry 2014;45:467–74.
[56]. Koo BS, Jung MJ, Lee JH, Jin HC, Lee JS, Kim YI. A pilot study of the correlation between the numeric rating scale used to evaluate geop and questionnaires on pain perception. Korean J Pain 2015;28:32–8.
[57]. Kopera M, Trucco EM, Suszek H, Kobylinski P, Wisniewski P, Wojnar M, Jakubczyk A. Pain sensitivity, negative affect, and alcohol use disorder status: a moderated mediation study of emotion dysregulation. J Clin Med 2021;10:1321.
[58]. Kregel J, Schumacher C, Dolphens M, Malfliet A, Goubert D, Lenoir D, Cagnie B, Meeus M, Coppieters I. Convergent validity of the Dutch central sensitization inventory: associations with psychophysical pain measures, quality of life, disability, and pain cognitions in patients with chronic spinal pain. Pain Pract 2018;18:777–87.
[59]. Kroenke K, Spitzer RL, Williams JBW. The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosomatic Med 2002;64:258–66.
[60]. Kuperman P, Granovsky Y, Bahouth H, Fadel S, Ben Lulu H, Bosak N, Buxbaum C, Sprecher E, Crystal S, Granot M. Explaining very early acute mild traumatic brain injury after motor vehicle collision pain variability: additive value of pain sensitivity questionnaire. Pain Rep 2020;5:e821.
[61]. Kuperman P, Granovsky Y, Granot M, Bahouth H, Fadel S, Hyams G, Ben Lulu H, Aspis O, Salame R, Begal J, Hochstein D, Grunner S, Honigman L, Reshef M, Sprecher E, Bosak N, Sterling M, Yarnitsky D. Psychophysic-psychological dichotomy in very early acute mTBI pain: a prospective study. Neurology 2018;91:e931–8.
[62]. La Touche R, Paris-Alemany A, Hidalgo-Pérez A, López-de-Uralde-Villanueva I, Angulo-Diaz-Parreño S, Muñoz-García D. Evidence for central sensitization in patients with temporomandibular disorders: a systematic review and meta-analysis of observational studies. Pain Pract 2018;18:388–409.
[63]. Lacourt TE, Houtveen JH, van Doornen LJP. Experimental pressure-pain assessments: test-retest reliability, convergence and dimensionality. Scand J Pain 2012;3:31–7.
[64]. Latremoliere A, Woolf CJ. Central sensitization: a generator of pain hypersensitivity by central neural plasticity. J Pain 2009;10:895–926.
[65]. Lautenbacher S, Krieg JC. Pain perception in psychiatric disorders: a review of the literature. J Psychiatr Res 1994;28:109–22.
[66]. Le Bars D, Dickenson AH, Besson JM. Diffuse noxious inhibitory controls (DNIC). I. Effects on dorsal horn convergent neurones in the rat. PAIN 1979;6:283–304.
[67]. Lee D. The convergent, discriminant, and nomological validity of the Depression Anxiety Stress Scales-21 (DASS-21). J Affective Disord 2019;259:136–42.
[68]. Lee E-H. Review of the psychometric evidence of the perceived stress scale. Asian Nurs Res 2012;6:121–7.
[69]. Li J, Simone DA, Larson AA. Windup leads to characteristics of central sensitization. PAIN 1999;79:75–82.
[70]. Lluch E, Duenas L, Falla D, Baert I, Meeus M, Sanchez-Frutos J, Nijs J. Preoperative pain neuroscience education combined with knee joint mobilization for knee osteoarthritis: a randomized controlled trial. Clin J Pain 2018;34:44–52.
[71]. Maier W, Buller R, Philipp M, Heuser I. The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. J Affective Disord 1988;14:61–8.
[72]. Mailloux C, Beaulieu L-D, Wideman TH, Massé-Alarie H. Within-session test-retest reliability of pressure pain threshold and mechanical temporal summation in healthy subjects. PLoS One 2021;16:e0245278.
[73]. Mansiz-Kaplan B, Ayhan FF, Cagli M, Atik F, Ece I. A preliminary study of the child abuse and central sensitization in adolescent patients with chronic non-organic chest pain and an overlooked condition: juvenile fibromyalgia syndrome. Pediatr Rheumatol 2020;18:28.
[74]. Mayer TG, Neblett R, Cohen H, Howard KJ, Choi YH, Williams MJ, Perez Y, Gatchel RJ. The development and psychometric validation of the central sensitization inventory. Pain Pract 2012;12:276–85.
[75]. McCracken LM, Zayfert C, Gross RT. The Pain Anxiety Symptoms Scale: development and validation of a scale to measure fear of pain. PAIN 1992;50:67–73.
[76]. McIntyre MH, Tung JY, Kless A, Hein P, Field M, Tung JY. Validity of the cold pressor test and pain sensitivity questionnaire via online self-administration. PLoS One 2020;15:e0231697.
[77]. McKernan LC, Finn MTM, Carr ER. Personality and affect when the central nervous system is sensitized: an analysis of central sensitization syndromes in a substance use disorder population. Psychodynamic Psychiatry 2017;45:385–409.
[78]. Meeker TJ, Schmid A-C, Liu Y, Keaser ML, Dorsey SG, Seminowicz DA, Greenspan JD. During capsaicin-induced central sensitization, brush allodynia is associated with baseline warmth sensitivity, whereas mechanical hyperalgesia is associated with painful mechanical sensibility, anxiety and somatization. Eur J Pain 2021;25:1971–93.
[79]. Meeus M, Nijs J. Central sensitization: a biopsychosocial explanation for chronic widespread pain in patients with fibromyalgia and chronic fatigue syndrome. Clin Rheumatol 2007;26:465–73.
[80]. Melotti R, Ruscheweyh R, Pramstaller PP, Hicks AA, Pattaro C. Structural consistency of the Pain Sensitivity Questionnaire in the cooperative health research in south tyrol (CHRIS) population-based study. J Pain 2018;19:1424–34.
[81]. Metzger RL. A reliability and validity study of the State-Trait Anxiety Inventory. J Clin Psychol 1976;32:276–8.
[82]. Mibu A, Nishigami T, Tanaka K, Manfuku M, Yono S. Difference in the impact of central sensitization on pain-related symptoms between patients with chronic low back pain and knee osteoarthritis. J Pain Res 2019;12:1757–65.
[83]. Midenfjord I, Grinsvall C, Koj P, Carnerup I, Tornblom H, Simren M. Central sensitization and severity of gastrointestinal symptoms in irritable bowel syndrome, chronic pain syndromes, and inflammatory bowel disease. Neurogastroenterol Motil 2021;33:e14156.
[84]. Miki T, Nishigami T, Takebayashi T, Yamauchi T. Association between central sensitivity syndrome and psychological factors in people with presurgical low back pain: a cross-sectional study. J Orthop Sci 2021;26:337–42.
[85]. Mikkonen J, Luomajoki H, Airaksinen O, Neblett R, Selander T, Leinonen V. Cross-cultural adaptation and validation of the Finnish version of the central sensitization inventory and its relationship with dizziness and postural control. BMC Neurol 2021;21:141.
[86]. Moore RL, Clifford AM, Moloney N, Doody C, Smart KM, O'Leary H. The relationship between clinical and quantitative measures of pain sensitization in knee osteoarthritis. Clin J Pain 2020;36:336–43.
[87]. Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 2011;34:601–8.
[88]. Neblett R. The central sensitization inventory: a user's manual. J Appl Biobehav Res 2018;23:e12123.
[89]. Neblett R, Hartzell MM, Cohen H, Mayer TG, Williams M, Choi Y, Gatchel RJ. Ability of the central sensitization inventory to identify central sensitivity syndromes in an outpatient chronic pain sample. Clin J Pain 2015;31:323–32.
[90]. Neblett R, Hartzell MM, Williams M, Bevers KR, Mayer TG, Gatchel RJ. Use of the Central Sensitization Inventory (CSI) as a treatment outcome measure for patients with chronic spinal pain disorder in a functional restoration program. Spine J 2017;17:1819–29.
[91]. Nguy V, Barry BK, Moloney N, Hassett LM, Canning CG, Lewis SJG, Allen NE. The associations between physical activity, sleep, and mood with pain in people with Parkinson's disease: an observational cross-sectional study. J Parkinsons Dis 2020;10:1161–70.
[92]. van der Noord R, Paap D, van Wilgen CP. Convergent validity and clinically relevant categories for the Dutch Central Sensitization Inventory in patients with chronic pain. J Appl Biobehav Res 2018;23:e12119.
[93]. Ohara PT, Vit J-P, Jasmin L. Cortical modulation of pain. Cell Mol Life Sci 2005;62:44–52.
[94]. Osman A, Barrios FX, Kopper BA, Hauptmann W, Jones J, O'Neill E. Factor structure, reliability, and validity of the pain catastrophizing scale. J Behav Med 1997;20:589–605.
[95]. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71.
[96]. Polli A, Ghosh M, Bakusic J, Ickmans K, Monteyne D, Velkeniers B, Bekaert B, Godderis L, Nijs J. DNA methylation and brain-derived neurotrophic factor expression account for symptoms and widespread hyperalgesia in patients with chronic fatigue syndrome and comorbid fibromyalgia. Arthritis Rheumatol 2020;72:1936–44.
[97]. Poutanen O, Koivisto A-M, Kääriä S, Salokangas RKR. The validity of the Depression Scale (DEPS) to assess the severity of depression in primary care patients. Fam Pract 2010;27:527–34.
[98]. Proença JDS, Baad-Hansen L, Braido GVdV, Mercante FG, Campi LB, Gonçalves DAdG. Lack of correlation between central sensitization inventory and psychophysical measures of central sensitization in individuals with painful temporomandibular disorder. Arch Oral Biol 2021;124:105063.
[99]. Quan X, Fong DYT, Leung AYM, Liao Q, Ruscheweyh R, Chau PH. Validation of the Mandarin Chinese version of the pain sensitivity questionnaire. Pain Pract 2018;18:180–93.
[100]. Quesada C, Kostenko A, Ho I, Leone C, Nochi Z, Stouffs A, Wittayer M, Caspani O, Brix Finnerup N, Mouraux A, Pickering G, Tracey I, Truini A, Treede R-D, Garcia-Larrea L. Human surrogate models of central sensitization: a critical review and practical guide. Eur J Pain 2021;25:1389–428.
[101]. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1:385–401.
[102]. Ramanaiah NV, Franzen M, Schill T. A psychometric study of the state-trait anxiety inventory. J Personal Assess 1983;47:531–5.
[103]. Rehberg B, Mathivon S, Combescure C, Mercier Y, Savoldelli GL. Prediction of acute postoperative pain following breast cancer surgery using the pain sensitivity questionnaire: a cohort study. Clin J Pain 2017;33:57–66.
[104]. Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, Treede R-D. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain 2006;10:77–88.
[105]. Roussel NA, Nijs J, Meeus M, Mylius V, Fayt C, Oostendorp R. Central sensitization and altered central pain processing in chronic low back pain: fact or myth? Clin J Pain 2013;29:625–38.
[106]. Ruscheweyh R, Dany K, Marziniak M, Gralow I. Basal pain sensitivity does not predict the outcome of multidisciplinary chronic pain treatment. Pain Med 2015;16:1635–42.
[107]. Ruscheweyh R, Marziniak M, Stumpenhorst F, Reinholz J, Knecht S. Pain sensitivity can be assessed by self-rating: development and validation of the Pain Sensitivity Questionnaire. PAIN 2009;146:65–74.
[108]. Ruscheweyh R, Verneuer B, Dany K, Marziniak M, Wolowski A, Colak-Ekici R, Schulte TL, Bullmann V, Grewe S, Gralow I, Evers S, Knecht S. Validation of the pain sensitivity questionnaire in chronic pain patients. PAIN 2012;153:1210–8.
[109]. Ruscheweyh R, Wersching H, Kugel H, Sundermann B, Teuber A. Gray matter correlates of pressure pain thresholds and self-rated pain sensitivity: a voxel-based morphometry study. PAIN 2018;159:1359–65.
[110]. Sanchis MN, Lluch E, Nijs J, Struyf F, Kangasperko M. The role of central sensitization in shoulder pain: a systematic literature review. Semin Arthritis Rheum 2015;44:710–6.
[111]. Sanders B, Becker-Lausen E. The measurement of psychological maltreatment: early data on the child abuse and trauma scale. Child Abuse Neglect 1995;19:315–23.
[112]. Schemer L, Korfer K, Glombiewski JA. Evaluation of exposure instructions to pain: should therapist focus on fear reduction or expectation violation? Cogn Ther Res 2020;44:697–708.
[113]. Schmidt FL, Hunter JE. Methods of meta-analysis: correcting error and bias in research findings. London: SAGE Publications, Ltd, 2015. doi: 10.4135/9781483398105.
[114]. Schotte CKW, Maes M, Cluydts R, De Doncker D, Cosyns P. Construct validity of the Beck depression inventory in a depressive population. J Affective Disord 1997;46:115–25.
[115]. Schuttert I, Timmerman H, Petersen KK, McPhee ME, Arendt-Nielsen L, Reneman MF, Wolff AP. The definition, assessment, and prevalence of (human assumed) central sensitization in patients with chronic low back pain: a systematic review. J Clin Med 2021;10:5931.
[116]. Sharma S, Jha J, Pathak A, Neblett R. Translation, cross-cultural adaptation, and measurement properties of the Nepali version of the central sensitization inventory (CSI). BMC Neurol 2020;20:286.
[117]. Shigetoh H, Koga M, Tanaka Y, Morioka S. Central sensitivity is associated with poor recovery of pain: prediction, cluster, and decision tree analyses. Pain Res Manag 2020;2020:1–12.
[118]. Shigetoh H, Tanaka Y, Koga M, Osumi M, Morioka S. The mediating effect of central sensitization on the relation between pain intensity and psychological factors: a cross-sectional study with mediation analysis. Pain Res Manag 2019;2019:1–6.
[119]. Shulman J, Zurakowski D, Keysor J, Jervis K, Sethna NF. Offset analgesia identifies impaired endogenous pain modulation in pediatric chronic pain disorders. PAIN 2020;161:2852–9.
[120]. Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, Bernard J. The brief resilience scale: assessing the ability to bounce back. Int J Behav Med 2008;15:194–200.
[121]. Starkweather AR, Heineman A, Storey S, Rubia G, Lyon DE, Greenspan J, Dorsey SG. Methods to measure peripheral and central sensitization using quantitative sensory testing: a focus on individuals with low back pain. Appl Nurs Res 2016;29:237–41.
[122]. StatsDirect Statistal Analysis Software. Available at: Accessed August 13, 2021.
[123]. Staud R, Robinson ME, Price DD. Temporal summation of second pain and its maintenance are useful for characterizing widespread central sensitization of fibromyalgia patients. J Pain 2007;8:893–901.
[124]. Steel PD, Kammeyer-Mueller JD. Comparing meta-analytic moderator estimation techniques under realistic conditions. J Appl Psychol 2002;87:96–111.
[125]. Terluin B, van Marwijk HW, Adèr HJ, de Vet HC, Penninx BW, Hermens ML, van Boeijen CA, van Balkom AJ, van der Klink JJ, Stalman WA. The Four-Dimensional Symptom Questionnaire (4DSQ): a validation study of a multidimensional self-report questionnaire to assess distress, depression, anxiety and somatization. BMC Psychiatry 2006;6:34.
[126]. Tkachuk GA, Harris CA. Psychometric properties of the Tampa scale for kinesiophobia-11 (TSK-11). J Pain 2012;13:970–7.
[127]. Treede R-D. The role of quantitative sensory testing in the prediction of chronic pain. PAIN 2019;160:S66–9.
[128]. Valeberg BT, Pedersen LM, Girotto V, Christensen VL, Stubhaug A. Validation of the Norwegian pain sensitivity questionnaire. J Pain Res 2017;10:1137–42.
[129]. Van Boekel RLM, Timmerman H, Bronkhorst EM, Ruscheweyh R, Vissers KCP, Steegers MAH. Translation, cross-cultural adaptation, and validation of the pain sensitivity questionnaire in Dutch healthy volunteers. Pain Res Manag 2020;2020:1–10.
[130]. Vanegas H, Vazquez E, Tortorici V. NSAIDs, opioids, cannabinoids and the control of pain by the central nervous system. Pharmaceuticals 2010;3:1335–47.
[131]. Verberne WR, Snijders TJ, Liem KS, Baakman AC, Veldhuijzen DS. Applications of ‘quantitative sensory testing’ [in Dutch]. Ned Tijdschr Geneeskd 2013;157:A5434.
[132]. Verbrugghe J, Agten A, Stevens S, Eijnde BO, Vandenabeele F, Roussel N, De Baets L, Timmermans A. Disability, kinesiophobia, perceived stress, and pain are not associated with trunk muscle strength or aerobic capacity in chronic nonspecific low back pain. Phys Ther Sport 2020;43:77–83.
[133]. Wachtel S, Vocks S, Edel M-A, Nyhuis P, Willutzki U, Teismann T. Validation and psychometric properties of the German capability for suicide questionnaire. Compr Psychiatry 2014;55:1292–302.
[134]. Walankar PP, Panhale VP, Patil MM. Psychosocial factors, disability and quality of life in chronic shoulder pain patients with central sensitization. Health Psychol Res 2020;8:8874.
[135]. Weaver KR, Griffioen MA, Klinedinst NJ, Galik E, Duarte AC, Colloca L, Resnick B, Dorsey SG, Renn CL. Quantitative sensory testing across chronic pain conditions and use in special populations. Front Pain Res 2021;2:779068.
[136]. van Wilgen CP, Vuijk PJ, Kregel J, Voogt L, Meeus M, Descheemaeker F, Keizer D, Nijs J. Psychological distress and widespread pain contribute to the variance of the central sensitization inventory: a cross-sectional study in patients with chronic pain. Pain Pract 2018;18:239–46.
[137]. Williams DA. Phenotypic features of central sensitization. J Appl Biobehav Res 2018;23:e12135.
[138]. Wolmeister AS, Schiavo CL, Nazário KCK, Castro SMdJ, de Souza A, Caetani RP, Caumo W, Stefani LC. The Brief Measure of Emotional Preoperative Stress (B-MEPS) as a new predictive tool for postoperative pain: a prospective observational cohort study. PLoS One 2020;15:e0227441.
[139]. Woolf CJ. Evidence for a central component of post-injury pain hypersensitivity. Nature 1983;306:686–8.
[140]. Woolf CJ, King AE. Dynamic alterations in the cutaneous mechanoreceptive fields of dorsal horn neurons in the rat spinal cord. J Neurosci 1990;10:2717–26.
[141]. Yarnitsky D, Granot M, Granovsky Y. Pain modulation profile and pain therapy: between pro- and antinociception. PAIN 2014;155:663–5.
[142]. Ye Q, Li X, Peng W. Individual variation in pain sensitivity and implicit negative bias toward pain. Psychosom Med 2020;82:796–804.
[143]. Yunus MB. Central sensitivity syndromes: a new paradigm and group nosology for fibromyalgia and overlapping conditions, and the related issue of disease versus illness. Semin Arthritis Rheum 2008;37:339–52.
[144]. Yunus MB. Role of central sensitization in symptoms beyond muscle pain, and the evaluation of a patient with widespread pain. Best Pract Res Clin Rheumatol 2007;21:481–97.
[145]. Zaorska J, Kopera M, Trucco EM, Suszek H, Kobyliński P, Jakubczyk A. Childhood trauma, emotion regulation, and pain in individuals with alcohol use disorder. Front Psychiatry 2020;11:554150.
[146]. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–70.

Central sensitization; Central sensitization; Central sensitivity syndromes; Quantitative sensory testing; Central sensitization inventory; Central sensitization Inventory; Pain Sensitivity Questionnaire; Hypervigilance; Meta-analyses; Systematic review

Supplemental Digital Content

© 2023 International Association for the Study of Pain