Jump to ContentJump to Main Navigation
Show Summary Details
More options …
New at De Gruyter

Scandinavian Journal of Pain

Official Journal of the Scandinavian Association for the Study of Pain

Editor-in-Chief: Breivik, Harald

4 Issues per year

CiteScore 2017: 0.84

SCImago Journal Rank (SJR) 2017: 0.401
Source Normalized Impact per Paper (SNIP) 2017: 0.452

See all formats and pricing
More options …
Volume 18, Issue 1

The validity of pain intensity measures: what do the NRS, VAS, VRS, and FPS-R measure?

Ivan S. K. Thong
  • Corresponding author
  • Blk 49 Hume Ave #06-04, Singapore 598749, Singapore, Phone: +65 9856 7379
  • National University of Singapore, Singapore, Singapore
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Mark P. Jensen / Jordi Miró / Gabriel Tan
Published Online: 2018-02-14 | DOI: https://doi.org/10.1515/sjpain-2018-0012


Background and aims:

The Numerical Rating Scale (NRS), Visual Analogue Scale (VAS), Verbal Rating Scale (VRS), and Faces Pain Scale-Revised (FPS-R) are valid measures of pain intensity. However, ratings on these measures may be influenced by factors other than pain intensity. The purpose of this study was to evaluate the influence of non-pain intensity factors on the pain intensity scales.


We administered measures of pain intensity (NRS, VAS, VRS, FPS-R), pain unpleasantness, catastrophizing, depressive symptoms, and pain interference to 101 individuals with chronic lower back or knee pain. Correlation analyses examined the associations among the pain intensity scales, and regression analyses evaluated the contributions of the non-pain intensity factors (depressive symptoms, and pain unpleasantness, catastrophizing, and interference) to the VAS, VRS, and FPS-R ratings, while controlling for NRS, age, and gender.


Although the NRS, VAS, VRS, FPR-S, scales were strongly associated with one another, supporting their validity as measures of pain intensity, regression analyses showed that the VRS also reflected pain interference, the FPS-R also reflected pain unpleasantness, and the VAS was not associated with any of the additional non-pain intensity factors when controlling for NRS, age, and gender.


The VAS appears to be most similar to the NRS and less influenced by non-pain intensity factors than the VRS or FPS-R. Although the VRS and FPS-R ratings both reflect pain intensity, they also contain additional information about pain interference and pain unpleasantness, respectively. These findings should be kept in mind when selecting pain measures and interpreting the results of research studies using these scales.


The influence of pain interference and pain unpleasantness on VRS and FPS-R, respectively should be kept in mind when selecting pain measures and interpreting the results of research studies using these scales.

This article offers supplementary material which is provided at the end of the article.

Keywords: pain assessment; pain intensity; pain rating; psychosocial factors

1 Introduction

Four commonly used pain intensity scales are the Numerical Rating Scales (NRSs), the Visual Analogue Scales (VASs), the Verbal Rating Scales (VRSs), and the Faces Pain Rating Scales (FPSs) [1], [2]. There is a general consensus that NRSs have more validity and more strengths than other scales [2], [3], [4], [5], [6], [7], [8]. However, there are situations where a VAS, VRS, or FPS may be more appropriate [9], [10], [11], [12], [13], [14], [15], [16], [17].

Pain intensity measures may be influenced by non-pain intensity factors. Qualitative studies have reported that some individuals consider non-pain intensity factors when rating pain intensity [18], [19]. It is also possible that the non-pain intensity factors that contribute to intensity ratings differ between scales. For example, researchers have traditionally graded pain intensity as reflected by the VRS with respect to pain’s interference with function [20], [21], [22]. It is possible that patients use a similar approach, and that VRS ratings may be influenced more strongly by pain interference than NRS ratings.

Also, VRSs and measures for pain interference and catastrophizing are assessed by questionnaires that depend heavily on verbal descriptions, as opposed to numbers. Therefore, VRSs may be more strongly associated with measures of pain catastrophizing and interference than NRSs. Preliminary support for this idea comes from a study which found that a composite score containing information about pain interference, pain catastrophizing, and other factors was associated with VRS ratings over and above the variance explained by NRS among adults with physical disabilities and chronic pain [23]. However, to our knowledge, this finding has not yet been replicated in additional pain populations.

Although the Faces Pain Scale – Revised (FPS-R) [24] was developed to measure pain intensity with a goal of minimizing affect cues, it is less strongly correlated with NRSs than other intensity measures [11], [25], [26], [27]. This may be due to the possibility that the facial expressions are viewed by respondents as representing emotional distress [28], [29], [30]. The FPS-R may therefore be more influenced by emotional distress than NRSs. However, to our knowledge, this possibility has not yet been examined.

As a measure with less verbal cues than VRS or affect-related cues than FPS-R, the VAS is more strongly associated with NRS than either VRS or FPS-R [4], [11], [31]. The VAS, like the NRS, may therefore be a more “pure” measure of pain intensity.

Information regarding whether pain intensity measures are influenced by non-pain intensity factors should be taken into account when selecting and interpreting measures. The objective of this study was therefore to examine whether VRS, FPS-R, and VAS ratings are associated with non-pain intensity factors. We hypothesized that the association between NRS and VAS would be stronger than the associations between these measures and a VRS or FPS-R. We also hypothesized that the VRS would be significantly associated with pain interference and pain catastrophizing, the FPS-R would be significantly associated with depressive symptomatology and pain unpleasantness, and the VAS would not be associated with any of these non-pain intensity factors, when controlling for NRS ratings.

2 Materials and methods

2.1 Participants

A convenience sample of 101 individuals with chronic pain were recruited through referrals from the National University Hospital’s (NUH) in Singapore: the Orthopedic Spine Clinic, the Anesthesia Pain Clinic and the Rheumatology Clinic. Participants were patients of the study’s referring physicians who were attending their medical appointments. Doctors referred participants who met the following inclusion criteria: (1) having a diagnosis of either primarily chronic (pain lasting for ≥3 months) low back or chronic knee pain; (2) reporting an average low back/knee pain intensity of 4 or greater on a 0–10 NRS; (3) being at least 21 years old; and (4) being able to read, speak, and write in English. Exclusion criteria were: (1) having cognitive impairments (e.g. dementia, intellectual disability) that would interfere with the ability to provide informed consent and complete the study measures; and (2) severe psychiatric or psychological problems that would interfere with participation.

2.2 Procedures

Potential participants were identified by NUH physicians and then screened again for eligibility by a research assistant stationed temporarily at the clinics. The research assistant described the study procedures to the potential participants, and those that were interested and were eligible were asked to sign an informed consent form. Participants were then asked to complete a packet of paper-and-pencil questionnaires assessing the study variables, described below. Ethical approval was obtained from the National Healthcare Group Domain Specific Review Board.

2.3 Measures

2.3.1 Average pain intensity

Four measures were used to assess average pain intensity: (1) a 0–10 NRS; (2) a Visual Analog Scale (VAS); (3) a VRS; and (4) the FPS-R [24]. With the NRS, participants were asked to rate their average pain intensity over the last 7 days by selecting a single number from 0 to 10. With the VAS, participants were asked to make a hatch mark on a 100 mm line that represents their average pain intensity over the last 7 days. With the FPS-R, participants were asked to rate their average pain over the last 7 days by selecting one of six line drawings of faces expressing an increasing level of pain intensity. These were then converted to a numerical score for each face (i.e. 0, 2, 4, 6, 8, or 10), depending on the face selected. The end-point descriptors for the NRS, VAS, and FPS-R were “No pain” (0, 0 mm, and the face representing no pain, respectively) and “The most intense pain imaginable” (10, 100 mm, and the face representing the most intense pain level, respectively). Finally, with the VRS, participants were asked to select one of four descriptors that represent four different levels of pain intensity (i.e. “None”, “Mild”, “Moderate”, and “Severe”). Each of these measures of pain intensity have a great deal of support for their reliability and validity when used with adults [2], [11], [32].

2.3.2 Pain unpleasantness

Participants were asked to rate their average pain unpleasantness over the last 7 days by selecting a single number from 0 to 10. The end-point descriptors were “Not unpleasant” (0) and “The most unpleasant pain imaginable” (10). This measure has been found to possess good convergent and discriminant validity, and sensitivity to change [33].

2.3.3 Depressive symptomatology

Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) [34]. The PHQ-9 asks respondents to rate the frequency of nine symptoms of depression over the past 2 weeks on 4-point Likert scales (with anchors: “Not at all” to “Nearly every day”) that reflect the nine DSM-IV criteria for major depression [35]. PHQ-9 scores can range from 0 to 27, with higher scores representing greater symptom severity. This scale has been widely used in clinical and research settings and thus much evidence supporting its validity is available [36], [37], [38]. Also, a strong correlation between Beck’s Depression Inventory II and the PHQ-9 has been reported, r=0.84, p<0.001 suggesting good convergent validity [39]. The reliability (internal consistency) of the PHQ-9 in the current sample was excellent (Cronbach’s α=0.92).

2.3.4 Catastrophizing

Pain catastrophizing was assessed using the 13-item Pain Catastrophizing Scale [40]. This scale asked participants to rate the degree to which they have catastrophizing thoughts and feelings when experiencing pain on 5-point Likert scales. A total score is computed by summing the responses to each item which can range from 0 to 52, with higher scores representing greater use of catastrophic thinking in response to pain. The PCS has been shown to have concurrent and discriminant validity, and high test-retest reliability over a 6-week period [40], [41], [42]. In the current sample, the internal consistency of the PCS was found to be excellent (Cronbach’s α=0.96).

2.3.5 Pain interference

Pain interference was measured using the four-item Pain Interference Short Form of the Patient-Reported Outcomes Measurement Information System (PROMIS) [43]. The items selected asked participants to rate the magnitude of pain interference with day-to-day activities, work around the home, ability to participate in social activities, and enjoyment of life. Each question is rated on a 5-point Likert scale (“Not at all” to “Very much”), and the responses to the items are summed to create a raw score that can range from 4 to 20. Like all PROMIS measures, the Pain Interference raw score can be converted to a standardized t-score representing the domain of interest, with a mean of 50 and standard deviation of 10 in the normative sample [43]. In the current sample, the internal consistency of the 4-item scale was found to be excellent (Cronbach’s α=0.90).

2.4 Data analysis

The number and percentages (for categorical variables), means and standard deviations (for continuous variables), or median and 25th and 75th percentile (for ordinal variables) of the demographic and study variables were first computed for descriptive purposes. We then computed Pearson’s correlations between the four pain intensity measures. Next, we performed a series of Steiger’s (1980) tests [44], [45] to test the hypothesis that the association between the NRS and VAS would be stronger than the associations between any other pair of pain intensity measures.

Finally, we performed three hierarchical regression analyses to evaluate the hypothesized associations between the VRS, FPS-R, and VAS and measures of pain interference, pain catastrophizing, depressive symptoms, and pain unpleasantness, when controlling for NRS ratings, age and gender. In these analyses, the VAS, VRS, and FPS-R pain intensity ratings were the criterion variables. Average pain intensity as measured by the NRS was entered in the first step. In the second step, age and gender were entered as control variables. We then entered the four independent variables (pain unpleasantness, depressive symptoms, pain catastrophizing, and pain interference) as a block in the third and final step. A p<0.05 was used to determine statistical significance. Due to the ordinal nature of VRS and FPS-R, non-parametric analyses (Spearman correlations and ordinal regression) were conducted as a way to determine if the findings would differ if non-parametric analyses were used instead of parametric analyses.

3 Results

3.1 Sample characteristics

A total of 101 participants were enrolled in the study. One of these was excluded from the analyses, as this participant’s questionnaire had a substantial amount of missing data. The characteristics of the 100 remaining participants in the study are listed in Table 1. Means and standard deviations, and median and interquartile range of the study variables in the sample are presented in Table 2.

Table 1:

Demographic and descriptive variables for the study sample.

Table 2:

Means and standard deviations or median and 25th and 75th percentile of the study variables.

3.2 Pearson’s correlation analyses

The Pearson’s correlation coefficients between each pair of pain intensity ratings are presented in Table 3. The correlation between the NRS and the VAS (r=0.93, see Table 3) was statistically significantly stronger than the correlation between any other pairs of measures (i.e. NRS/VAS correlation versus: NRS/VRS correlation, z=5.82, p=<0.05; NRS/FPS-R correlation, z=5.95, p=<0.05; VAS/VRS correlation, z=6.71, p=<0.05; VAS/FPS-R correlation, z=6.89, p=<0.05; and VRS/FPS-R correlation z=6.91, p=<0.05). None of the other pairs of correlation coefficients were significantly different from one another. The results of the non-parametric analyses examining the associations among the pain ratings were essentially the same as the parametric analyses (see Supplementary Table 1).

Table 3:

Pearson’s correlation between the pain intensity scales.

3.3 Linear regression analyses

3.3.1 Predicting VAS ratings

Table 4 presents the results of the regression analysis. With VAS as the criterion variable, the findings show a direct positive effect of NRS on VAS in the first step (β=0.93; t=24.65, p<0.05). In the second step, neither age nor sex contributed significantly to the prediction of the VAS ratings. In the third step, none of the independent variables, pain unpleasantness, depressive symptoms, pain catastrophizing, and pain interference, were statistically significant.

Table 4:

Results of the linear regression analyses.

3.3.2 Predicting VRS ratings

With VRS as the criterion variable, a direct positive effect of NRS on VRS can be seen (β=0.76; t=11.62, p<0.05) in the first step (see Table 4). Only age (β=0.16; t=2.23, p<0.05), but not sex, was statistically significant in the second step. In the third step, pain interference made a statistically significant (β=0.19; t=2.30, p<0.05) unique contribution to the prediction of the VRS ratings. Pain unpleasantness, depressive symptoms, and pain catastrophizing were not statistically significant in the third step. The results of the non-parametric (ordinal regression) analyses predicting VRS ratings from the same predictors used in the linear regression analyses were essentially the same as those from the parametric analyses (see Supplementary Table 2).

3.3.3 Predicting FPS-R ratings

With FPS-R as the criterion variable, the findings show a direct positive effect of pain NRS on FPS-R in the first step (β=0.75; t=11.21, p<0.05; see Table 4). In the second step, age and sex as a block explained 3% of the variance in FPS-R above and beyond NRS. In the third step, only pain unpleasantness made a statistically significant (β=0.30; t=2.67, p<0.05) and independent contribution to the prediction of the FPS-R ratings. Depressive symptoms, pain catastrophizing, and pain interference were not statistically significant in the third step. The results of the non-parametric (ordinal regression) analyses predicting FPS-R ratings from the same predictors used in the linear regression analyses were essentially the same as those from the parametric analyses (see Supplementary Table 2).

4 Discussion

Consistent with the study hypotheses, we found that the strongest association among the four pain intensity measures was between the NRS and VAS. We also found, as hypothesized, that the VRS and FPS-R ratings were associated significantly with pain interference and pain unpleasantness, respectively, after controlling for NRS ratings. Also, the VAS was not associated with any of the potential confounding variables evaluated, once NRS ratings were controlled. However, and inconsistent with the study hypotheses, the VRS was not found to be associated with pain catastrophizing and FPS-R was not associated with depressive symptoms, once NRS ratings were controlled. The findings have important implications for the interpretation of pain intensity as measured by the NRS, VAS, VRS, and FPS-R, in clinical and research settings.

The current findings are consistent with the idea that the VAS and NRS are “more pure” (although not necessarily completely “pure” cf. [18], [19]) measures of pain intensity than either the VRS or FPS-R. This idea is supported by the very strong association between the NRS and VAS (r=0.93), indicating that they measure essentially the same thing, as well as by the finding consistent with this strong association that none of the study predictors were associated with the VAS once the NRS was controlled. This finding is also consistent with past research which has shown that the associations between the NRS and the VAS are stronger than their associations with either the VRS or the FPS-R [4], [11], [31]. However, given that the VAS possesses a number of significant limitations not shared with the NRS [8], [46], [47], such as requiring physical equipment (e.g. pen and paper or an interactive device) as well as the need for respondents to have adequate levels of motor skills, visual acuity, and abstract thinking, the NRS can still be considered as the first choice measure of pain intensity when the population to be studied can use it reliably [2].

The study findings also suggest that the VRS and FPS-R, while they share as much as 56% and 59% of the variance with the NRS, respectively, also appear to share variance with pain interference and pain unpleasantness, respectively. These findings are in line with past research which has shown that the VRS ratings are influenced by pain interference (at least when entered as a composite score with other variables) [23], and the FPS can be viewed as representing emotional responses [29], [30].

Thus, when individuals with chronic pain report their pain as being “severe” on a VRS, they may not be merely communicating that the intensity of their pain is of a high magnitude, but also communicating that the pain is interfering with their lives. Similarly, when individuals choose a facial drawing of a higher magnitude on the FPS-R, they may be communicating – at least to some degree – how they are affected emotionally by the pain. This knowledge may help in the interpretation of study findings with respect to the effects of pain treatment on these measures. For example, some of the benefits in “pain intensity” following cognitive behavioral therapy for chronic pain (which involves methods aimed directly at the thoughts associated with pain, the avoidance of unpleasant thoughts and of painful experiences, and the beliefs about pain and their relationship with behavior [48]) as measured by the VRS or the FPS-R may be due, at least in part, to the changes in pain-related domains other than just pain intensity (i.e. pain interference and pain unpleasantness, respectively). Researchers should therefore keep in mind the factors that contribute to these pain intensity ratings when interpreting research studies using the VRS and FPS-R.

Future research should examine the generalizability of this study’s results to populations where a VRS or FPS-R may be more appropriate than a NRS or VAS (e.g. some elderly individuals, individuals with severe cognitive impairment or pediatric samples) [9], [10], [11], [12], [13], [14], [15], [16], [17], [49]. There is a possibility that the influence of pain interference and pain unpleasantness on the VRS and FPS-R may be even greater among these populations than the population of patients studied here. For example, researchers have found that right hemispheric stroke patients reported that the FPS was more a measure of sadness than pain, while the opposite pattern was found in non-stroke controls [29].

Despite the positive findings with respect to the role that pain interference has on VRS ratings and pain unpleasantness has on FPS-R ratings, the study hypotheses regarding the influence of pain catastrophizing and depressive symptomatology on VRS and FPS-R ratings, respectively, were not supported. The null finding with respect to the VRS was unexpected as a previous study found the potential influence of pain catastrophizing on VRS scores [23]. The difference in findings may be due to the fact that a composite score (containing information about pain catastrophizing and other variables) was used and pain catastrophizing was assessed with a different measure in the previous study. The null finding with respect to a role for depression in FPS-R ratings was surprising given past research with adults and elderly patients indicating that the FPS-R can be viewed as representing sadness [29], [30]. The null finding in the current study may have been due to a possible floor effect in depression in the sample. As the sample size was relatively small, the null findings with respect to both the VRS and FPS-R may have also been due to limited power.

There are several limitations of this study that should be considered when interpreting the results. First, the sample size (n=100) was relatively small. The relatively low sample size may have limited the power to test the study hypotheses; for example, significant associations between catastrophizing and VRS ratings or between depressive symptoms and FPS-R ratings might have emerged had the sample size been larger. Therefore, it would be useful to replicate this study with larger samples. Second, the use of a convenience sample of patients being treated in a pain clinic may have biased the sample in ways that we cannot determine. Thus, replicating the findings in additional samples of individuals with chronic pain would help to determine their reliability. Third, our sample consisted of mostly middle-aged participants who were cognitively intact with primarily chronic lower back and/or knee pain. The extent to which the findings generalize to populations where the VRS and FPS-R are more likely to be needed and used (e.g. very elderly individuals and/or individuals with cognitive deficits) or to populations with other forms of pain is not known. This provides another reason for the need to replicate the findings in additional samples. Fourth, given the fact that the VRS and FPS-R measures are ordinal and may lack ratio qualities, it would be reasonable to question the validity of using parametric analyses with these measures. At the same time, if we had limited the analyses to parametric tests for to predict the VAS scores and non-parametric tests to predict the VRS and FPS-R scores, this would have limited our ability to directly compare the findings across measures or to previous studies which used parametric analyses [11], [23], [50]. To address this issue, we also performed non-parametric analyses. In support of the accuracy of the parametric analysis results, the results of the non-parametric analyses were essentially the same. Finally, the sample consisted mostly of individuals with only mild depression at the most [34]. As alluded to previously, this may have produced a floor effect (restriction of range in depression scores) contributing to the negative findings regarding the influence of depressive symptoms on the FPS-R.

Despite the study’s limitations, the results provide important new information regarding the potential influence of domains (other than pain intensity) on VRS, FPS-R, and VAS ratings. The findings suggest that the VAS provides a measure of pain intensity very similar to the NRS that is less influenced by beliefs about pain or distress than the VRS or FPS-R. On the other hand, VRS and FPS-R ratings of pain intensity appear to reflect both pain intensity (as measured by the NRS) as well as pain interference and pain unpleasantness, respectively. Future research is needed to evaluate the generalizability of these findings in older, younger, and cognitively impaired individuals, where the VRS and FPS-R scales are more likely to be used.


  • [1]

    Hjermstad MJ, Gibbins J, Haugen DF, Caraceni A, Loge JH, Kaasa S. Pain assessment tools in palliative care: an urgent need for consensus. Palliative Med 2008;22:895–903. CrossrefWeb of ScienceGoogle Scholar

  • [2]

    Jensen MP, Karoly P. Self report scales and procedures for assessing pain in adults. In: Turk DC, Melzack R. editors. Handbook of Pain Assessment. New York: Guilford Press, 2011. Google Scholar

  • [3]

    Chanques G, Viel E, Constantin JM, Jung B, de Lattre S, Carr J, Cissé M, Lefrant J, Jaber S. The measurement of pain in intensive care unit: comparison of 5 self-report intensity scales. Pain 2010;151:711–21. PubMedCrossrefWeb of ScienceGoogle Scholar

  • [4]

    Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, Fainsinger R, Aass N, Kaasa S, European Palliative Care Research Collaborative. Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. J Pain Symptom Manage 2011;41:1073–93. CrossrefPubMedGoogle Scholar

  • [5]

    Jensen MP, Karoly P, Braver S. The measurement of clinical pain intensity: a comparison of six methods. Pain 1986;27:117–26. PubMedCrossrefGoogle Scholar

  • [6]

    Jensen MP, Karoly P, O’riordan EF, Bland F, Burns RS. The subjective experience of acute pain. An assessment of the utility of 10 indices. Clin J Pain 1989;5:153–60. CrossrefPubMedGoogle Scholar

  • [7]

    Sánchez-Rodríguez E, Miró J, Castarlenas E. A comparison of four self-report scales of pain intensity in 6- to 8-year-old children. Pain 2012;153:1715–9. Web of ScienceCrossrefPubMedGoogle Scholar

  • [8]

    Von Korff M, Jensen MP, Karoly P. Assessing global pain severity by self-report in clinical and health services research. Spine 2000;25:3140–51. PubMedCrossrefGoogle Scholar

  • [9]

    Bird J. Selection of pain measurement tools. Nurs Stand 2003;18:33–9. PubMedCrossrefGoogle Scholar

  • [10]

    Hadjistavropoulos T, Herr K, Prkachin KM, Craig KD, Gibson SJ, Lukas A, Smith JH. Pain assessment in elderly adults with dementia. Lancet Neurol 2014;13:1216–27. CrossrefWeb of SciencePubMedGoogle Scholar

  • [11]

    Ferreira-Valente MA, Pais-Ribeiro JL, Jensen MP. Validity of four pain intensity rating scales. Pain 2011;152:2399–404. CrossrefWeb of SciencePubMedGoogle Scholar

  • [12]

    Herr KA, Mobily PR. Comparison of selected pain assessment tools for use with the elderly. App Nurs Res 1993;6:39–46. CrossrefGoogle Scholar

  • [13]

    Ho K, Spence J, Murphy MF. Review of pain-measurement tools. Ann Emerg Med 1996;27:427–32. CrossrefPubMedGoogle Scholar

  • [14]

    Herr KA, Garand L. Assessment and measurement of pain in older adults. Clin Geriatr Med 2001;17:457. CrossrefPubMedGoogle Scholar

  • [15]

    Miró J, Huguet A, Nieto R, Paredes S, Baos J. Evaluation of reliability, validity and preference for a pain intensity scale for use with the elderly. J Pain 2005;6:727–35. CrossrefPubMedGoogle Scholar

  • [16]

    Stuppy DJ. The Faces Pain Scale: reliability and validity with mature adults. App Nurs Res 1998;11:84–9. CrossrefGoogle Scholar

  • [17]

    Taylor LJ, Harris J, Epps CD, Herr K. Psychometric evaluation of selected pain intensity scales for use with cognitively impaired and cognitively intact older adults. Rehabil Nurs 2005;30:55–61. CrossrefPubMedGoogle Scholar

  • [18]

    Robinson-Papp J, George MC, Dorfman D, Simpson DM. Barriers to chronic pain measurement: a qualitative study of patient perspectives. Pain Med 2015;16:1256–64. Web of SciencePubMedCrossrefGoogle Scholar

  • [19]

    Williams AC, Davies HT, Chadury Y. Simple pain rating scales hide complex idiosyncratic meanings. Pain 2000;85:457–63. PubMedCrossrefGoogle Scholar

  • [20]

    Jensen MP, Smith DG, Ehde DM, Robinsin LR. Pain site and the effects of amputation pain: further clarification of the meaning of mild, moderate, and severe pain. Pain 2001;91:317–22. PubMedCrossrefGoogle Scholar

  • [21]

    Serlin RC, Mendoza TR, Nakamura Y, Edwards KR, Cleeland CS. When is cancer pain mild, moderate or severe? Grading pain severity by its interference with function. Pain 1995;61:277–84. CrossrefPubMedGoogle Scholar

  • [22]

    Zelman DC, Hoffman DL, Seifeldin R, Dukes EM. Development of a metric for a day of manageable pain control: derivation of pain severity cut-points for low back pain and osteoarthritis. Pain 2003;106:35–42. CrossrefGoogle Scholar

  • [23]

    Jensen MP, Tomé-Pires C, de la Vega R, Galán S, Solé E, Miró J. What determines whether a pain is rated as mild, moderate, or severe? The importance of pain beliefs and pain interference. Clin J Pain 2017;33:414–21. PubMedCrossrefWeb of ScienceGoogle Scholar

  • [24]

    Hicks CL, von Baeyer CL, Spafford PA, van Korlaar I, Goodenough B. The Faces Pain Scale–Revised: toward a common metric in paediatric pain measurement. Pain 2001;93:173–83. CrossrefGoogle Scholar

  • [25]

    Ware LJ, Epps CD, Herr K, Packard A. Evaluation of the revised faces pain scale, verbal descriptor scale, numeric rating scale, and Iowa pain thermometer in older minority adults. Pain Manag Nurs 2006;7:117–25. PubMedCrossrefGoogle Scholar

  • [26]

    Sánchez-Rodríguez E, de la Vega R, Castarlenas E, Roset R, Miró J. An app for the assessment of pain intensity: validity properties and agreement of pain reports when used with young people. Pain Med 2015;16:1982–92. Web of SciencePubMedCrossrefGoogle Scholar

  • [27]

    Sánchez-Rodríguez E, Castarlenas E, de la Vega R, Roset R, Miró J. On the electronic measurement of pain intensity: can we use different pain intensity scales interchangeably? J Health Psychol 2016;22:1658–67. Web of SciencePubMedGoogle Scholar

  • [28]

    Bieri D, Reeve RA, Champion GD, Addicoat L, Ziegler JB. The Faces Pain Scale for the self-assessment of the severity of pain experienced by children: development, initial validation, and preliminary investigation for ratio scale properties. Pain 1990;41:139–50. CrossrefPubMedGoogle Scholar

  • [29]

    Benaim C, Froger J, Cazottes C, Gueben D, Porte M, Desnuelle C, Pelissier JY. Use of the Faces Pain Scale by left and right hemispheric stroke patients. Pain 2007;128:52–8. Web of ScienceCrossrefPubMedGoogle Scholar

  • [30]

    Herr KA, Mobily PR, Kohout FJ, Wagenaar D. Evaluation of the Faces Pain Scale for use with the elderly. Clin J Pain 1998;14:29–38. CrossrefPubMedGoogle Scholar

  • [31]

    Li L, Liu X, Herr K. Postoperative pain intensity assessment: a comparison of four scales in Chinese adults. Pain Med 2007;8:223–34. PubMedCrossrefWeb of ScienceGoogle Scholar

  • [32]

    Williamson A, Hoggart B. Pain: a review of three commonly used pain rating scales. J Clin Nurs 2005;14:798–804. CrossrefGoogle Scholar

  • [33]

    Pagé MG, Katz J, Stinson J, Isaac L, Martin-Pichora AL, Campbell F. Validation of the numerical rating scale for pain intensity and unpleasantness in pediatric acute postoperative pain: sensitivity to change over time. J Pain 2012;13:359–69. PubMedWeb of ScienceCrossrefGoogle Scholar

  • [34]

    Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606–13. PubMedCrossrefGoogle Scholar

  • [35]

    American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th ed. Washington: American Psychiatric Association, 1994. Google Scholar

  • [36]

    Bombardier CH, Richards JS, Krause JS, Tulsky D, Tate DG. Symptoms of major depression in people with spinal cord injury: implications for screening. Arch Phys Med Rehabil 2004;85:1749–56. CrossrefPubMedGoogle Scholar

  • [37]

    Fann JR, Bombardier CH, Dikmen S, Esselman P, Warms CA, Pelzer E, Rau H, Temkin N. Validity of the Patient Health Questionnaire-9 in assessing depression following traumatic brain injury. J Head Trauma Rehabil 2005;20:501–11. PubMedCrossrefGoogle Scholar

  • [38]

    Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA. Psychometric characteristics of the multidimensional scale of perceived social support. J Pers Assess 1990;55:610–7. CrossrefPubMedGoogle Scholar

  • [39]

    Dum M, Pickren J, Sobell LC, Sobell MB. Comparing the BDI-II and the PHQ-9 with outpatient substance abusers. Addict Behav 2008;33:381–7. Web of SciencePubMedCrossrefGoogle Scholar

  • [40]

    Sullivan MJ, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess 1995;7:524. CrossrefGoogle Scholar

  • [41]

    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. PubMedCrossrefGoogle Scholar

  • [42]

    Van Damme S, Crombez G, Bijttebier P, Goubert L, Van Houdenhove B. A confirmatory factor analysis of the Pain Catastrophizing Scale: invariant factor structure across clinical and non-clinical populations. Pain 2002;96:319–24. CrossrefPubMedGoogle Scholar

  • [43]

    Amtmann D, Cook KF, Jensen MP, Chen WH, Choi S, Revicki D, Cella D, Rothrock N, Keefe F, Callahan L, Lai JS. Development of a PROMIS item bank to measure pain interference. Pain 2010;150:173–82. Web of ScienceCrossrefPubMedGoogle Scholar

  • [44]

    Lee IA, Preacher KJ. Calculation for the test of the difference between two dependent correlations with one variable in common [Computer software]. Available at http://quantpsy.org/calc.htm. Accessed: 6 Jan 2018. 

  • [45]

    Steiger JH. Tests for comparing elements of a correlation matrix. Psychol Bull 1980;87:245–51. CrossrefGoogle Scholar

  • [46]

    Breivik H, Borchgrevink PC, Allen SM, Rosseland LA, Romundstad L, Breivik Hals EK, Kvarstein G, Stubhaug A. Assessment of pain. Br J Anaesth 2008;101:17–24. Web of ScienceCrossrefPubMedGoogle Scholar

  • [47]

    Holdgate A, Asha S, Craig J, Thompson J. Comparison of a verbal numeric rating scale with the visual analogue scale for the measurement of acute pain. Emerg Med 2003;15:441–6. CrossrefGoogle Scholar

  • [48]

    Williams AC, Eccleston C, Morley S. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst Rev 2012;11:CD007407. PubMedGoogle Scholar

  • [49]

    Tomlinson D, von Baeyer CL, Stinson JN, Sung L. A systematic review of faces scales for the self-report of pain intensity in children. Pediatrics 2010;126:e1168–98. CrossrefWeb of SciencePubMedGoogle Scholar

  • [50]

    Aziato L, Dedey F, Marfo K, Asamani JA, Clegg-Lamptey JN. Validation of three pain scales among adult postoperative patients in Ghana. BMC Nurs 2015;14:42. CrossrefWeb of SciencePubMedGoogle Scholar

Supplemental Material:

The online version of this article offers supplementary material (https://doi.org/10.1515/sjpain-2018-0012).

About the article

Received: 2018-01-09

Accepted: 2018-01-11

Published Online: 2018-02-14

Published in Print: 2018-01-26

Authors’ statements

Research funding: This project was supported by a start-up grant of Dr. Tan from the National University of Singapore.

Conflict of interest: None of the authors has any potential conflict of interest with the paper.

Informed consent: Informed consent was obtained from all participants.

Ethical approval: Ethical approval was obtained from the National Healthcare Group Domain Specific Review Board.

Citation Information: Scandinavian Journal of Pain, Volume 18, Issue 1, Pages 99–107, ISSN (Online) 1877-8879, ISSN (Print) 1877-8860, DOI: https://doi.org/10.1515/sjpain-2018-0012.

Export Citation

©2018 Scandinavian Association for the Study of Pain. Published by Walter de Gruyter GmbH, Berlin/Boston. All rights reserved..Get Permission

Supplementary Article Materials

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Yu Heng Kwan, Amanda Ng, Ka Keat Lim, Warren Fong, Jie Kie Phang, Eng Hui Chew, Nai Lee Lui, Chuen Seng Tan, Julian Thumboo, Truls Østbye, and Ying Ying Leung
Rheumatology International, 2018
Jolanda Ehrström, Jyrki Kettunen, and Petri Salo
Scandinavian Journal of Pain, 2018, Volume 18, Number 4, Page 593
Dragana Ceprnja and Amitabh Gupta
Physiotherapy Research International, 2018, Page e1746

Comments (0)

Please log in or register to comment.
Log in