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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Pain Med. Author manuscript; available in PMC 2013 October 15.
Published in final edited form as:
PMCID: PMC3796944

Measurement of Affective and Activity Pain Interference Using the Brief Pain Inventory (BPI): Cancer and Leukemia Group B 70903*

Thomas M. Atkinson, PhD,1 Susan Halabi, PhD,2 Antonia V. Bennett, PhD,3 Lauren Rogak, MA,3 Laura Sit, BA,3 Yuelin Li, PhD,1 Ellen Kaplan, MA,2 Ethan Basch, MD, MSc,3 and for the Cancer and Leukemia Group B



The Brief Pain Inventory (BPI) was designed to yield separate scores for pain intensity and interference. It has been proposed that the pain interference factor can be further broken down into unique factors of affective (e.g., mood) and activity (e.g., work) interference. The purpose of this analysis was to confirm this affective/activity interference dichotomy.

Patients and Methods

A retrospective confirmatory factor analysis was completed for a sample of 184 individuals diagnosed with castrate-resistant prostate cancer (Age 40–86, M = 65.46, 77% White Non-Hispanic) who had been administered the BPI as part of Cancer and Leukemia Group B (CALGB) trial 9480. A one-factor model was compared against two-factor and three-factor models that were developed based on the design of the instrument.


Root mean squared error of approximation (0.075), comparative fit index (0.971), and change in chi-square, given the corresponding change in degrees of freedom (13.33, p < .05) values for the three-factor model (i.e., pain intensity, activity interference, and affective interference) were statistically superior in comparison to the one- and two-factor models. This three-factor structure was found to be invariant across age, mean PSA and hemoglobin levels.


These results confirm that the BPI can be used to quantify the degree to which pain separately interferes with affective and activity aspects of a patient's everyday life. These findings will provide clinical trialists, pharmaceutical sponsors, and regulators with confidence in the flexibility of the BPI as they consider the use of this instrument to assist with understanding the patient experience as it relates to treatment.

Keywords: Measurement, Cancer Pain, Quality of Life


The Brief Pain Inventory (BPI), developed three decades ago to assess pain and its impact in cancer [1, 2], has since become the most widely used measure of pain as a patient-reported outcome (PRO) [3]. This instrument has been translated and validated in a number of different languages [418] and disease types [1925] and was recently shown [3] to satisfy most key psychometric aspects of the United States Food and Drug Administration’s (FDA) Guidance for Industry on the use of PRO Measures in Medical Product Development to Support Labeling [26].

While the BPI was originally designed to measure pain intensity (i.e., self-perceived magnitude of pain at a given time interval) and pain interference (i.e., the degree to which pain impacts various aspects of typical daily functioning), the test developers demonstrated initial evidence that the BPI’s pain interference construct is multidimensional and represents distinct activity and affective interference factors [18]. Activity pain interference refers to the degree to which experienced pain impacts events such as work or walking ability, whereas affective pain interference encompasses the impact that pain has on an individual’s mood or ability to enjoy life. This activity/affective interference dichotomy was consistent with previously proposed pain models [27].

We previously retrospectively evaluated this hypothetical factor structure in independent samples of individuals with HIV/AIDS or cancer who had been administered the BPI [28]. A confirmatory factor analysis suggested preliminarily that the three-factor model was statistically superior to alternative unitary or two-factor models. While the results of our previous study helped to further establish the viability of a three-factor model, it contained a number of methodological shortcomings that limited its generalizability to patients receiving active therapy for cancer: 1) the “Interference with Work” item from the BPI was not administered because it was not relevant for the particular sample; 2) the previous sample was in a palliative care context; and 3) the study used data from patients who were in a routine care setting rather than enrolled in a clinical trial [29].

Therefore, to build upon our previous findings while addressing methodological concerns, the purpose of the present study is to evaluate the full 11-item version of the BPI in a sample of patients enrolled in a multi-institutional Phase III clinical trial. It was predicted that the pain intensity/affective interference/activity interference model would best fit this data, which would support the use of this model as part of clinical research and ultimately would provide investigators, pharmaceutical sponsors, and regulators with confidence in the flexibility of this instrument.



The study sample consisted of a retrospective review of 184 patients diagnosed with metastatic castrate-resistant prostate cancer (CRPC). These patients had been randomized to low, intermediate, or high doses of suramin as part of enrollment in a Phase III Cancer and Leukemia Group B trial (CALGB 9480) that was collected between February 1996 and July 1998. Patients were eligible for CALGB 9480 if they had progressive metastatic adenocarcinoma of the prostate, a life expectancy of at least three months, performance status of 0–2, and adequate hematologic, renal, hepatic, and clotting function. As an additional eligibility requirement for the current study, patients were eligible if their “worst pain the last 24-hours” was ≥ 3. This added constraint made 206 of the original 390 enrolled patients (53%) ineligible for the present analysis (CALGB 70903). Informed consent was obtained from each patient. All ethical guidelines were followed as required for conducting human research.


Brief Pain Inventory [1, 2, 30] – The BPI is an 11-item questionnaire that consists of four 0-to-10 numeric rating scale (NRS) items asking patients to rate their pain at its “worst in the last 24-hours,” “least in the last 24-hours,” “average,” and “now,” with a 0 indicating “no pain” and 10 representing “pain as bad as you could imagine.” The remaining seven BPI items probe the degree to which pain interferes with general activity, mood, walking ability, normal work, relations with other people, sleep, and enjoyment of life, again using a 0-to-10 NRS. For these interference items, 0 represents “does not interfere” and 10 indicates “interferes completely.”

Statistical Analysis

Confirmatory factor analysis (CFA) was used to investigate construct validity of the BPI. Several fit indices were selected in order to test which CFA model best represents the present dataset: root-mean-squared error of approximation (RMSEA) [31], comparative fit index (CFI) [32], chi-square, and change in chi-square given the change in degrees of freedom between models. RMSEA is a measure of the average of the residual variance and covariance; good models have RMSEA values that are at or less than .08 [33]. CFI is an index that falls between 0 and 1, with values greater than .90 considered to be indicators of good fitting models [33]. When comparing models, a lower chi-square value indicates a better fit, given an equal number of degrees of freedom.

Based upon the hypothetical underlying constructs for the BPI, [1, 18] three models were developed to represent the best fit for the overall data. Model 1 was a one-factor model used as a baseline comparison against the other models. Model 2 was a two-factor model with pain severity and pain interference treated as latent factors. For Model 3, pain severity was again treated as a latent factor, with pain interference treated as two separate factors of activity interference (i.e., general activity, normal work, walking ability) and affective interference (i.e., mood, relations with other people, sleep, enjoyment of life).

A multi-group structural analysis was used in order to investigate whether the three factors from the CFA were invariant across age and laboratory values (i.e., prostate-specific antigen, hemoglobin, and alkaline phosphatase) [43]. Age was treated as a binary variable, with the overall sample divided into those above (n = 92) or below (n = 92) the median age of 68.58. Laboratory values were also treated as binary variables with equal groups of 92 patients above and below the median values for prostate-specific antigen (164ng/mL), hemoglobin (12.3 g/dL), and alkaline phosphatase (196 IU/L), respectively.


The 184 male patients in the dataset were aged 40–86 (M = 68.46, SD = 8.61) with advanced prostate cancer diagnoses, receiving treatment on CALGB 9840 [3441]. Patient characteristics are displayed in Table 1. The sample consisted largely of Caucasian (76.6%) and African-American (16.9%) patients, with a smaller proportion identifying themselves as Hispanic (4.9%) or “other” (1.6%).

Table 1
Characteristics of Patients (N = 184)

Table 2 displays the means, standard deviations, and Pearson correlation coefficients among the eleven items of the BPI. The present dataset satisfied all CFA requirements for normality, multicollinearity, residual values, and multivariate outliers.

Table 2
Correlation Coefficients, Means, and Standard Deviations for Outcome Measures

During initial model specification, modification indices were examined to determine whether any of the residual variances had strong inter-correlations. Modification indices represent the expected chi-square change if a parameter if the model is freed [42]. Consequently, adding correlations between residuals improves model fit. Strong correlations between the residual variances were observed as follows: “pain at its worst in the last 24 hours” and “describe your pain on average”; “pain at its least in the last 24 hours” and “describe your pain on average”; “pain at its least in the last 24 hours” and “how much pain do you have right now”; “describe your pain on average” and “how much pain do you have right now”; “interference with general activity” and “interference with mood”; “interference with mood” and “interference with relations with other people”; “interference with walking ability” and “interference with work”; and “interference with walking ability” and “interference with sleep.” For the subsequent analyses, these correlations were added to increase the overall fit for each model.

According to the fit indices (Table 3), Model 2 was a significant improvement over Model 1. Model 2 had a lower RMSEA value (0.096), a higher CFI value (0.950), and a significant change in chi-square given the corresponding change in degrees of freedom when compared to Model 1 (χ2(1) = 17.75, p < .05). Model 3 was statistically superior to Model 2 in terms of RMSEA (0.075), CFI (0.971), and change in chi-square given the corresponding change in degrees of freedom values (χ2(2) = 13.33, p < .05). From these results, Model 3 was selected as the best fit for the data (Figure 1), with Model 2 treated as a suitable alternative representation in this sample. Standardized factor loadings for all models are displayed in Table 4.

Figure 1
Confirmatory Factor Model for the Three-Factor Solution
Table 3
Fit Indices for Confirmatory Factor Models in Overall Sample
Table 4
Standardized Factor Loadings and Factor Correlations by Model and Latent Construct (N = 184)

Multi-Group Analysis

For each of the four multi-group analyses, a model was fit that simultaneously imposed constraints on all of the factor loadings and covariances. Placing these constraints forced the values to be equal across groups. This model was then compared to a baseline model where none of the factor loadings were constrained. Table 5 is a display of the fit indices for the multi-group analyses. Since for each of the analyses there were no significant differences between the models in chi-square value, given the corresponding change in degrees of freedom, there was evidence that the three-factor solution from the CFA was invariant across groups.

Table 5
Multi-Group Analysis Fit Indices by Age, PSA, Hgb and Alkaline Phosphatase Groups by Factor Loading Constraints for the Three-Factor Solution


Pain can differentially impact both patients’ general activity and affective experiences; these are clearly two different domains that should be measured independently. To measure only one of these areas independently gives an incomplete picture. Moreover, only evaluating pain intensity does not give the full picture of the patient experience. The present analysis is the first to demonstrate that the BPI can be used to simultaneously capture pain intensity, affective pain interference, and activity pain interference in patients enrolled in a clinical trial. This information can be of particular interest to clinicians who are evaluating the impact of a given treatment and could assist them with making decisions related to balancing and/or minimizing the degree to which an intervention impacts affective functioning or every day activities.

There are a number of limitations of this study. While this was a multi-institutional Phase III randomized clinical trial, this was an all male sample in a single disease type. Analyses should be completed in more diverse patient samples as well as other disease types before these findings can be further generalized. Additionally, this study lacked the inclusion of additional measures of affective and activity functioning, which would have further strengthened the argument that the BPI is sensitive to the measurement of these distinct areas. While this is in no way minimizes the argument that the BPI can quantify patient-reported outcomes in pain intensity, affective interference, and activity interference, a future investigation that included such additional, established measures would be useful.

A natural next step for this line of research would be to determine whether the BPI could be substantially reduced to from its current 11-items down to three items that would specifically represent the domains of interest (e.g., pain at its worst in the last 24 hours, interference with general activity, interference with enjoyment of life) in order to reduce both patient burden and more efficiently capture this patient-reported information. Such a study would prospectively determine whether a single item can capture the full spectrum of information about the nature of pain intensity or affective/activity interference. Finally, in research contexts where the full BPI is administered, it should be determined whether the results for these three factors can be reported separately, as they provide information about the patient experience and could ultimately be linked to overall survival [44, 45].


Department of Psychiatry and Behavioral Sciences, Memorial Sloan-Kettering Cancer Center, New York, NY; supported by CA35113

Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC; supported by CA47577

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY; supported by CA60138


*The research for CALGB 70903 was supported in part by grants from the National Cancer Institute (CA31946) to the Cancer and Leukemia Group B (Monica M. Bertagnolli, MD, Chair) and to the CALGB Statistical Center (Daniel J. Sargent, PhD, CA33601) as well as National Institutes of Health Support Grant P30-CA-008748. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute. The authors have no financial relationships to disclose.


1. Daut RL, Cleeland CS. The prevalence and severity of pain in cancer. Cancer. 1982;50:1913–1918. [PubMed]
2. Daut RL, Cleeland CS, Flanery RC. Development of the Wisconsin Brief Pain Questionnaire to assess pain in cancer and other diseases. Pain. 1983;17:197–210. [PubMed]
3. Atkinson TM, Mendoza TR, Sit L, Passik S, Scher HI, Cleeland C, et al. The Brief Pain Inventory and its "Pain at its Worst in the Last 24 Hours" Item: Clinical Trial Endpoint Considerations. Pain Medicine. 2010;11:337–346. [PMC free article] [PubMed]
4. Yun YH, Mendoza TR, Heo DS, Yoo T, Heo BY, Park H-A, et al. Development of a cancer pain assessment tool in Korea: A validation of a Korean version of the Brief Pain Inventory. Oncology. 2004;66:439–444. [PubMed]
5. Klepstad P, Loge JH, Borchgrevink PC, Mendoza TR, Cleeland CS, Kaasa S. The Norwegian Brief Pain Inventory questionnaire: Translation and validation in cancer pain patients. J Pain Symptom Manage. 2002;24:517–525. [PubMed]
6. Radbruch L, Loick G, Kiencke P, Lindena G, Sabatowski R, Grond S, et al. Validation of the German version of the Brief Pain Inventory. J Pain Symptom Manage. 1999;18:180–187. [PubMed]
7. Badia X, Muriel C, Gracia A, Núñez-Olarte JM, Perulero N, Gálvez R, et al. Validación española del cuestionario Brief Pain Inventory en pacientes con dolor de causa neoplásica. Med Clin (Barc) 2003;120:52–59. [PubMed]
8. Caraceni A, Mendoza TR, Mencaglia E, Baratella C, Edwards K, Forjaz MJ, et al. A validation study of an Italian version of the Brief Pain Inventory (Breve Questionario Per La Valutazione Del Dolore) Pain. 1996;65:87–92. [PubMed]
9. Ger L-P, Ho S-T, Sun W-Z, Wang M-S, Cleeland CS. Validation of the Brief Pain Inventory in a Taiwanese population. J Pain. 1999;9:105–121. [PubMed]
10. Ballout S, Noureddine S, Huijer HA, Kanazi G. Psychometric evaluation of the Arabic Brief Pain Inventory in a sample of Lebanese cancer patients. J Pain Symptom Manage. 2011;42:147–154. [PubMed]
11. Cleeland CS, Ladinsky JL, Serlin RC, Nugyen CT. Multidimensional measurement of cancer pain: Comparison of U.S. and Vietnamese patients. J Pain Symptom Manage. 1988;3:23–27. [PubMed]
12. Kalyadina SA, Ionova TI, Ivanova MO, Uspenskaya OS, Kishtovich AV, Mendoza TR, et al. Russian Brief Pain Inventory: Validation and application in cancer pain. J Pain Symptom Manage. 2008;35:95–102. [PubMed]
13. Larue F, Colleau SM, Brasseur L, Cleeland CS. Multicentre study of cancer pain and its treatment in France. BMJ. 1995;310:1034–1037. [PMC free article] [PubMed]
14. Mystakidou K, Mendoza T, Tsilika E, Befon S, Parpa E, Bellos G, et al. Greek Brief Pain Inventory: Validation and utility in cancer pain. Oncology. 2001;60:35–42. [PubMed]
15. Saxena A, Mendoza T, Cleeland CS. The assessment of cancer pain in North India: The validation of the Hindi Brief Pain Inventory – BPI-H. J Pain Symptom Manage. 1999;18:27–41. [PubMed]
16. Uki J, Mendoza T, Cleeland CS, Nakamura Y, Takeda F. A brief cancer pain assessment tool in Japanese: The utility of the Japanese Brief Pain Inventory – BPI-J. J Pain Symptom Manage. 1998;16:364–373. [PubMed]
17. Wang XS, Mendoza TR, Gao SZ, Cleeland CS. The Chinese version of the Brief Pain Inventory (BPI-C): Its development and use in a study of cancer pain. Pain. 1996;67:407–416. [PubMed]
18. Cleeland CS, Nakamura Y, Mendoza TR, Edwards KR, Douglas J, Serlin RC. Dimensions of the impact of cancer pain in a four country sample: New information from multidimensional scaling. Pain. 1996;67:267–273. [PubMed]
19. Tan G, Jensen MP, Thornby JI, Shanti BF. Validation of the Brief Pain Inventory for Chronic Nonmalignant Pain. J Pain. 2004;5:133–137. [PubMed]
20. Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the Brief Pain Inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20:309–318. [PubMed]
21. Tittle MB, McMillian SC, Hagan S. Validating the Brief Pain Inventory for use with surgical patients with cancer. Oncol Nurs Forum. 2003;30:325–330. [PubMed]
22. Gjeilo KH, Stenseth R, Wahba A, Lydersen S, Klepstad P. Validation of the Brief Pain Inventory in patients six months after cardiac surgery. J Pain Symptom Manage. 2007;34:648–656. [PubMed]
23. Williams VSL, Smith MY, Fehnel SE. The validity and utility of the BPI interference measures for evaluating the impact of osteoarthritic pain. J Pain Symptom Manage. 2006;31:48–57. [PubMed]
24. Mendoza T, Mayne T, Rublee D, Cleeland C. Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. Eur J Pain. 2006;10:353–361. [PubMed]
25. Osborne TL, Raichle KA, Jensen MP, Ehde DM, Kraft G. The reliability and validity of pain interference measures in persons with Multiple Sclerosis. J Pain Symptom Manage. 2006;32:217–229. [PubMed]
26. US Food and Drug Administration. Guidance for Industry. Patient-reported outcome measures: Use in medical development to support labeling claims. 2009 [PMC free article] [PubMed]
27. Price DD, Harkins SW, Baker C. Sensory-affective relationships among different types of clinical and experimental pain. Pain. 1987;28:297–307. [PubMed]
28. Atkinson TM, Rosenfeld BD, Sit L, Mendoza TR, Fruscione M, Lavene D, et al. Using confirmatory factor analysis to evaluate construct validity of the Brief Pain Inventory (BPI) J Pain Symptom Manage. 2011;41:558–565. [PMC free article] [PubMed]
29. Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, et al. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain. 2005;113 [PubMed]
30. Cleeland CS. Research in cancer pain. What we know and what we need to know. Cancer. 1991;67:823–827. [PubMed]
31. Browne MW, Cudeck R. Alternative ways of assessing model fit. Newbury Park, CA: Sage; 1993.
32. Bentler PM. Comparative fit indixes in structural models. Psychol Bull. 1990;107:238–246. [PubMed]
33. Hu L, Bentler PM. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6:1–55.
34. Small EJ, Halabi S, Ratain MJ, Rosner B, Stadler W, Palchak D, et al. Randomized study of three different doses of Suramin administered with a fixed dosing schedule in patients with advanced prostate cancer: Results of Intergroup 0159, Cancer and Leukemia Group B 9480. J Clin Oncol. 2002;20:3369–3375. [PubMed]
35. Bok RA, Halabi S, Fei DT, Rodriguez CR, Hayes DF, Vogelzang NJ, et al. Vascular endothelial growth factor and basic fibroblast growth factor urine levels as predictors of outcome in hormone-refractory prostate cancer patients: A Cancer and Leukemia Group B study. Cancer Res. 2001;15:2533–2536. [PubMed]
36. Kantoff PW, Halabi S, Farmer DA, Hayes DF, Vogelzang NJ, Small EJ. Prognostic significance of Reverse Transcriptase Polymerase Chain Reaction for Prostate-Specific Antigen in men with hormone-refractory prostate cancer. 2001;19:3025–3028. 2001. [PubMed]
37. George DJ, Halabi S, Shepard TF, Sanford B, Vogelzang NJ, Small EJ, et al. The prognostic significance of Plasma Interleukin-6 levels in patients with metastatic hormone-refractory prostate cancer: Results from Cancer and Leukemia Group B 9480. Clin Cancer Res. 2005;11:1815–1820. [PubMed]
38. George DJ, Halabi S, Shepard TF, Vogelzang NJ, Hayes DF, Small EJ, et al. Prognostic significance of Plasma Vascular Endothelial Growth Factor levels in patients with hormone-refractory prostate cancer treated on Cancer and Leukemia Group B 9480. Clin Cancer Res. 2001;7:1932–1936. [PubMed]
39. Taplin M-E, George DJ, Halabi S, Sanford B, Febbo PG, Hennessy KT, et al. Prognostic significance of Plasma Chromogranin A levels in patients with hormone-refractory prostate cancer treated in Cancer and Leukemia Group B 9480 study. Urology. 2005;66:386–391. [PubMed]
40. Humphrey PA, Halabi S, Picus J, Sanford B, Vogelzang NJ, Small EJ, et al. Prognostic significance of Plasma Scatter Factor/Hepatocyte Growth Factor levels in patients with metastatic hormone-refractory prostate cancer: Results from Cancer and Leukemia Group B 150005/9480. Clin Genitourin Cancer. 2006;4:269–274. [PubMed]
41. Halabi S, Small EJ, Hayes DF, Vogelzang NJ, Kantoff P. Prognostic significance of Reverse Transcriptase Polymerase Chain Reaction for Prostate-Specific Antigen in metastatic prostate cancer: A nested study within CALGB 9583. J Clin Oncol. 2003;21:490–495. [PubMed]
42. Loehlin JC. Latent variable models: An introduction to factor, path, and structural equation analysis. 4th edn. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.; 2004.
43. Dolan CV, Molenaar PC. Testing specific hypotheses concerning latent group differences in multi-group covariance structure analysis with structured means. Multivariate Behav Res. 1994;29:203–222.
44. Ahles TA, Herndon JE, Small EJ, Vogelzang NJ, Kornblith AB, Ratain MJ, et al. Quality of life impact of three different doses of suramin in patients with metastatic hormone-refractory prostate carcinoma: Results of Intergroup 0159/Cancer and Leukemia Group B 9480. Cancer. 2004;101:2202–2208. [PubMed]
45. Halabi S, Vogelzang NJ, Kornblith AB, Ou S-S, Kantoff PW, Dawson NA, et al. Pain predicts overall survival in men with metastatic castration-refractory prostate cancer. J Clin Oncol. 2008;15:2544–2549. [PubMed]