Download this measure in English. BACK PROMIS SF v1.0-Dyspnea-Functional Limitations 10a English Downloads Download PDF Download DOC

Interpretation OF THE FACIT MEASURES Due to the evolving nature of QOL research, the best approach to interpreting data collected with a FACIT measure is to conduct a comprehensive literature search to determine the approaches taken by others and build upon that body of work. Development The majority of FACIT measures have undergone a standard scale development and validation methodology, which takes place in four phases: item generation, item reduction, scale construction, and psychometric evaluation. The scale development process involves considerable input from patients and expert health care providers, using a semi-structured interview designed to elicit personal experiences and educated opinions about how a disease, treatment, or condition may affect physical status, emotional well-being, functional well-being, family/social issues, sexuality/intimacy, work status, and future orientation. This process yields an exhaustive list of candidate items, which then undergo a series of reviews and reductions based on patient and expert ratings and item quality. A finite set of targeted concerns are then derived. Final candidate items are formatted with response choices compatible with a 5-point Likert-type scale, and appended to the FACT-G. Newly constructed FACIT subscales then undergo an initial assessment of reliability and validity using a sample of at least 50 patients. The validation design typically involves patient completion of a baseline assessment, a test-retest assessment 3–7 days later, and a third assessment 2–3 months later to demonstrate sensitivity to change over time. Relevant sociodemographic and treatment data is also collected and a battery of other measures administered at the baseline and 2–3 month retest to help determine convergent and divergent validity. A comprehensive analysis of the data gathered (including item response theory modeling when sample size allows) yields useful psychometric information and establishes initial reliability and validity of the scale. Further details regarding the development and validation of specific FACIT measures can be found in the literature. Reference Values Reference values are population values of a PRO instrument which can be a particular disease population or the general population. They are also often useful if generated for a particular political or geographical designation, e.g., at the country level. Such values can be useful for putting scores of an individual or group into context. Typically, reference values include averages, dispersion (e.g., standard deviation), ranges, or other aspects of the scores’ distributions. They are often reported for an overall sample and for key demographic groups (e.g., by age and sex). Reference values are most useful if they are estimated using a representative sample of patients, regardless of whether that is for the general population or a particular disease sample. Reference values can be applied usefully in both research and clinical settings. There have been multiple reports of reference values for FACIT instruments. In addition to the FACT-G, reference values have been published for the FACT-General Population (FACT-GP; general population sample); FACT Kidney Symptom Index instruments (FKSI; general population sample); FACIT-Fatigue (general population sample); FACT-Cognitive Function (FACT-Cog; healthy population); and the FACIT-Spiritual Wellbeing Scale (FACIT-Sp-12). We recommend that these reference values be used for comparison to scores from future research. Clinical and Other Anchors Anchor variables are very useful tools to help interpret FACIT score differences and change. Anchors are external criterion variables on which the magnitude of change on the construct of interest is well-understood and therefore can be used to “anchor” an interpretation of difference or change on the PRO of interest. Anchors are useful for multiple important applications in PRO-based research. First, anchors are used to test known-groups validity and responsiveness to change in the process of establishing a PRO’s psychometric properties. Second, and more germane to the interpretation of FACIT measures, there is now general consensus that anchor-based approaches are most appropriate for establishing thresholds for important differences and important changes at the group level. In this case, “differences” refer to cross-sectional, between-groups comparisons, and “changes” refer to within-group comparisons over time. Finally, anchoring PROs to clinically familiar differences and changes can help translate their meaning to patients and clinicians. Multiple types of anchors are useful for establishing important differences and changes. There is significant focus on patient reported anchors. Patient-reported anchors have the advantage of utilizing the same assessment method, and they typically assess changes that are meaningful to patients. In addition, when the patient-reported anchor represents the same construct as the PRO, we have more confidence that the difference or change estimates derived from an analysis using the anchor are relevant to the PRO. However, other types of anchors may be useful as well, especially in cancer research. For example, clinical variables that are not the same construct as the PRO but have a demonstrable relationship with the PRO, such as adverse events, tumor response, or progression, may be useful as well. However, any anchor used should be sufficiently correlated with the PRO to justify its use. We require a minimum correlation of 0.30 to justify use of an anchor; although correlations above 0.40 are preferred, as we have noted a paradox by which anchors with lower correlations tend to produce smaller estimates of important difference or change. Because this is essentially an exercise in acquiring multiple converging points of evidence, we advise use of multiple anchors that include patient report, clinician report, and objective clinical metrics (e.g., laboratory values; radiographic data). Important Differences and Change At the group level, determining the level of difference that is considered important to patients or other stakeholders over and above statistical significance can enhance interpretation because, with large sample sizes, even trivial differences can be statistically significant. Important difference estimates can be used to determine whether patient groups differ in HRQoL, and may be especially useful for planning future studies by providing a basis for power analyses. Similarly, important change estimates can indicate the amount of change that patients find meaningful or that indicate clinically important improvements or decrements. A previous summary of important differences and changes on FACIT instruments found relative consistency in the magnitude important differences in terms of proportion of the total scale points. In summary, the following ranges for important differences were found: FACT-G Total: 4–7% of total scores (3–7 units), FACT-G subscales: 7–11% (2–3 units), symptom-targeted instrument totals (e.g., Total FACT-Anemia, Total FACT-Breast, Total FACT-Colorectal, Total FACT-Head and Neck): 4–8% (5–12 units), and trial outcome indexes (e.g., Fatigue, Anemia, Biological Response Modifiers, Breast, Colorectal, Lung): 5–7% (4–7 units). This was a thorough aggregation of data up to 2005, but many studies estimating important differences for FACIT instruments, especially newer instruments or for non-cancer populations, have been published since that time. After collecting 15 additional years of data, these 2005 estimates have held true. We recommend that researchers consult the literature for up-to-date and appropriate important difference or change estimates for any given FACT or FACIT scale of interest. To implement this recommendation, it is important to use estimates of important change that have come from longitudinal studies actually focusing on change over time in the FACT or FACIT scale of interest, instead of substituting a cross-sectional estimate of the important difference where an estimate of important change is needed. There are a few reasons to distinguish between change versus difference estimates. First, analyses to estimate important change typically use change scores (i.e., difference between baseline and a post-baseline follow-up), which may be distributed differently than FACT/FACIT scale scores at a single cross-sectional cut. Second, the analyses used to determine change often differ from analyses to estimate important differences in some ways. Identifying important changes in terms of meaningfulness to patients is required to support the use of FACT/FACIT instruments in regulatory applications. The FDA, for one, has prioritized estimating meaningful change thresholds for PROs using patient-reported anchors that measure the same construct or domain of the PRO to be used as an endpoint in trials to show treatment benefit. A very common anchor for this kind of application is the patient global impression of change (PGIC), which retrospectively asks the patients how much they have changed on a domain of interest over a clinically relevant period of time and a set of discreet response options to characterize this change. Then, the difference in mean PRO change scores can be examined over the PGIC response options to determine the amount of change on the PRO associated with meaningful categories as defined on the PGIC, e.g., difference in mean PRO change scores between patients reporting being “about the same” and “a little worse” on the PGIC anchor. To help interpret these differences, empirical cumulative distribution plots (eCDF) can be created and plotted to represent change on the PRO within each anchor category. A useful alternative to the PGIC may be to examine prospective change in a similar item, the patient global impression of severity (PGIS), which assesses the level of symptom severity at a given time point. Responder Definition An important step in interpreting a PRO is to identify the responder definition, or the amount of change at the individual level that should be interpreted as treatment benefit. Used alone, group-level estimates of change on PROs may not be appropriate for classifying individuals as having changed. Identifying responders to treatment requires determining whether the change for an individual patient is significant, and group-level estimates of change (e.g., from important difference or change analyses) may under-estimate this. This view is in contrast to current regulatory focus on defining responders in terms of meaningful change based on a patient-reported anchor; such methods are necessarily group-based, focusing on identifying the average change for the group of individuals who said they changed on an anchor. In contrast to this approach, other authors have argued that, “a minimum standard for saying an individual has responded (improved) should include that the change in score is statistically significant.” Since it often requires large changes, statistically significant change at the individual level may also be meaningful to the individual. Interpretation Higher scores for the scales and subscales indicate better quality of life. Average FACT-G scores for a group of patients can be compared to normative data to determine the HRQOL of the patients relative to the general U.S. population. These comparisons facilitate meaningful interpretation of HRQOL in patient populations. Though the body of literature is constantly evolving, normative data typically does not exist for disease-, symptom-, or condition-specific subscales. FACIT measures have been shown to be responsive to change in both clinical and observational studies. Minimally important differences (MIDs) for scores of scales and subscales for some measures are available in the literature. An MID is the "smallest difference in score in the domain of interest that patients perceive as important, either beneficial or harmful, and that would lead the clinician to consider a change in the patient's management". MID estimates may vary across patients and possibly across patient groups; thus, ranges of MIDs have been identified for some scales, though it’s best to check the literature. For more information about any of the above, please refer to: Webster, K.A., Peipert, J.D., Lent, L.F., Bredle, J., Cella, D. (2022). The Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System: Guidance for Use in Research and Clinical Practice. In: Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_6 Further Reading Webster, K.A., Peipert, J.D., Lent, L.F., Bredle, J., Cella, D. (2022). The Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System: Guidance for Use in Research and Clinical Practice. In: Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_6 T. Pearman, B. Yanez, J. Peipert, K. Wortman, J. Beaumont, and D. Cella, "Ambulatory cancer and US general population reference values and cutoff scores for the functional assessment of cancer therapy," Cancer, vol. 120, no. 18, pp. 2902-2909, 2014. P. S. Brucker, K. Yost, J. Cashy, K. Webster, and D. Cella, "General population and cancer patient norms for the Functional Assessment of Cancer Therapy-General (FACT-G)," Evaluation & the health professions, vol. 28, no. 2, pp. 192-211, 2005. Z. Butt, J. Peipert, K. Webster, C. Chen, and D. Cella, "General population norms for the functional assessment of cancer therapy–Kidney Symptom Index (FKSI)," Cancer, vol. 119, no. 2, pp. 429-437, 2013. B. Holzner et al., "Normative data for functional assessment of cancer therapy general scale and its use for the interpretation of quality of life scores in cancer survivors," Acta Oncologica, vol. 43, no. 2, pp. 153-160, 2004. M. Janda, T. DiSipio, C. Hurst, D. Cella, and B. Newman, "The Queensland cancer risk study: general population norms for the Functional Assessment of Cancer Therapy–General (FACT‐G)," Psycho‐Oncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer, vol. 18, no. 6, pp. 606-614, 2009. A.-S. L. Bagge, A. Carlander, C. Fahlke, and R. O. Bagge, "Health-Related Quality of Life (FACT-GP) in General Swedish Population," European Journal of Surgical Oncology, vol. 46, no. 2, pp. e7-e8, 2020. I. Montan, B. Löwe, D. Cella, A. Mehnert, and A. Hinz, "General population norms for the functional assessment of chronic illness therapy (FACIT)-Fatigue Scale," Value in Health, vol. 21, no. 11, pp. 1313-1321, 2018. D. Cella, J. s. Lai, C. H. Chang, A. Peterman, and M. Slavin, "Fatigue in cancer patients compared with fatigue in the general United States population," Cancer, vol. 94, no. 2, pp. 528-538, 2002. D. Cella, M. J. Zagari, C. Vandoros, D. D. Gagnon, H.-J. Hurtz, and J. W. Nortier, "Epoetin alfa treatment results in clinically significant improvements in quality of life in anemic cancer patients when referenced to the general population," Journal of Clinical Oncology, vol. 21, no. 2. M. Lange, N. Heutte, N. Morel, F. Eustache, F. Joly, and B. Giffard, "Cognitive complaints in cancer: The French version of the Functional Assessment of Cancer Therapy–Cognitive Function (FACT-Cog), normative data from a healthy population," Neuropsychological rehabilitation, vol. 26, no. 3, pp. 392-409, 2016. J.-S. Lai et al., "Parent-perceived child cognitive function: results from a sample drawn from the US general population," Child's Nervous System, vol. 27, no. 2, pp. 285-293, 2011. A. R. Munoz, J. M. Salsman, K. D. Stein, and D. Cella, "Reference values of the Functional Assessment of Chronic Illness Therapy‐Spiritual Well‐Being: A report from the American Cancer Society's studies of cancer survivors," Cancer, vol. 121, no. 11, pp. 1838-1844, 2015. G. R. Norman, F. G. Sridhar, G. H. Guyatt, and S. D. Walter, "Relation of distribution-and anchor-based approaches in interpretation of changes in health-related quality of life," Medical care, pp. 1039-1047, 2001. D. Cella, D. T. Eton, J.-S. Lai, A. H. Peterman, and D. E. Merkel, "Combining anchor and distribution-based methods to derive minimal clinically important differences on the Functional Assessment of Cancer Therapy (FACT) anemia and fatigue scales," Journal of pain and symptom management, vol. 24, no. 6, pp. 547-561, 2002. R. R. Hay, D., "Reliability and validity (including responsiveness)," in Assessing Quality of Life in Clinical Trials: Methods and Practice, P. F. R. Hays Ed., 2nd ed. Oxford, NY: Oxford University Press, 2005, pp. 525-539. T. Devji et al., "Evaluating the credibility of anchor based estimates of minimal important differences for patient reported outcomes: instrument development and reliability study," bmj, vol. 369, 2020. K. J. Yost and D. T. Eton, "Combining distribution-and anchor-based approaches to determine minimally important differences: the FACIT experience," Evaluation & the health professions, vol. 28, no. 2, pp. 172-191, 2005. D. Victorson, M. Soni, and D. Cella, "Metaanalysis of the correlation between radiographic tumor response and patient‐reported outcomes," Cancer: Interdisciplinary International Journal of the American Cancer Society, vol. 106, no. 3, pp. 494-504, 2006. P. M. Fayers and R. D. Hays, "Don’t middle your MIDs: regression to the mean shrinks estimates of minimally important differences," Quality of Life Research, vol. 23, no. 1, pp. 1-4, 2014. J. M. Salsman, J. L. Beaumont, K. Wortman, Y. Yan, J. Friend, and D. Cella, "Brief versions of the FACIT-fatigue and FAACT subscales for patients with non-small cell lung cancer cachexia," Supportive Care in Cancer, vol. 23, no. 5, pp. 1355-1364, 2015. P. Rebelo, A. Oliveira, L. Andrade, C. Valente, and A. Marques, "Minimal Clinically Important Differences for Patient-Reported Outcome Measures of Fatigue in Patients With COPD Following Pulmonary Rehabilitation," Chest, vol. 158, no. 2, pp. 550-561, 2020. S. N. Garland et al., "Prospective evaluation of the reliability, validity, and minimally important difference of the functional assessment of cancer therapy‐gastric (FACT‐Ga) quality‐of‐life instrument," Cancer, vol. 117, no. 6, pp. 1302-1312, 2011. J. D. Peipert et al., "Validation of the Functional Assessment of Cancer Therapy–Leukemia instrument in patients with acute myeloid leukemia who are not candidates for intensive therapy," Cancer, vol. 126, no. 15, pp. 3542-3551, 2020. M. T. King, M. Agar, D. C. Currow, J. Hardy, B. Fazekas, and N. McCaffrey, "Assessing quality of life in palliative care settings: head-to-head comparison of four patient-reported outcome measures (EORTC QLQ-C15-PAL, FACT-Pal, FACT-Pal-14, FACT-G7)," Supportive Care in Cancer, vol. 28, no. 1, pp. 141-153, 2020. S. Yount et al., "A randomized validation study comparing embedded versus extracted FACT Head and Neck Symptom Index scores," Quality of Life Research, vol. 16, no. 10, pp. 1615-1626, 2007. D. Cella et al., "Validity of the FACT Hepatobiliary (FACT-Hep) questionnaire for assessing disease-related symptoms and health-related quality of life in patients with metastatic pancreatic cancer," Quality of Life Research, vol. 22, no. 5, pp. 1105-1112, 2013. D. Cella et al., "What is a clinically meaningful change on the functional assessment of Cancer therapy–lung (FACT-L) questionnaire?: results from eastern cooperative oncology group (ECOG) study 5592," Journal of clinical epidemiology, vol. 55, no. 3, pp. 285-295, 2002. D. Cella, M. B. Nichol, D. Eton, J. B. Nelson, and P. Mulani, "Estimating clinically meaningful changes for the Functional Assessment of Cancer Therapy—Prostate: results from a clinical trial of patients with metastatic hormone-refractory prostate cancer," Value in Health, vol. 12, no. 1, pp. 124-129, 2009. J. Steel, D. T. Eton, D. Cella, M. Olek, and B. Carr, "Clinically meaningful changes in health-related quality of life in patients diagnosed with hepatobiliary carcinoma," Annals of Oncology, vol. 17, no. 2, pp. 304-312, 2006. R. Jaeschke, J. Singer, and G. H. Guyatt, "Measurement of health status. Ascertaining the minimal clinically important difference," (in eng), Control Clin Trials, vol. 10, no. 4, pp. 407-15, Dec 1989, doi: 10.1016/0197-2456(89)90005-6. H. L. Cheng et al., "Psychometric testing of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group—Neurotoxicity (FACT/GOG-Ntx) subscale in a longitudinal study of cancer patients treated with chemotherapy," Health and quality of life outcomes, vol. 18, no. 1, pp. 1-9, 2020. S.-F. Wong et al., "A prospective study to validate the functional assessment of cancer therapy (FACT) for epidermal growth factor receptor inhibitor (EGFRI)-induced dermatologic toxicities FACT-EGFRI 18 questionnaire: SWOG S1013," Journal of patient-reported outcomes, vol. 4, no. 1, pp. 1-12, 2020. U. F. a. D. Administration, "Discussion Document for Patient-Focused Drug Development Public Workshop on Guidance 4: Incorporating Clinical Outcome Assessments into Endpoints for Regulatory Decision-Making," United States Department of Health and Human Services, Silver Spring, MD, 2019. U. F. a. D. Administration, "Discussion Document for Patient-Focused Drug Development Public Workshop on Guidance 3: Select, Develop or Modify Fit-for-Purpose Clinical Outcome Assessments," United States Department of Health and Human Services, Silver Spring, MD, 2018. R. E. Jensen et al., "Validation of the PROMIS physical function measures in a diverse US population-based cohort of cancer patients," Quality of life research, vol. 24, no. 10, pp. 2333-2344, 2015. R. E. Jensen et al., "Responsiveness of 8 Patient‐Reported Outcomes Measurement Information System (PROMIS) measures in a large, community‐based cancer study cohort," Cancer, vol. 123, no. 2, pp. 327-335, 2017. C. D. Coon and K. F. Cook, "Moving from significance to real-world meaning: methods for interpreting change in clinical outcome assessment scores," Quality of Life Research, vol. 27, no. 1, pp. 33-40, 2018. H. R. D. P. J. D, "Minimally Important Differences Do Not Identify Responders to Treatment," JOJ Sciences, Juniper Publishers Inc., vol. 1, no. 1, pp. 4-5, 2018. G. R. Norman, P. Stratford, and G. Regehr, "Methodological problems in the retrospective computation of responsiveness to change: the lesson of Cronbach," Journal of clinical epidemiology, vol. 50, no. 8, pp. 869-879, 1997. L. D. McLeod, C. D. Coon, S. A. Martin, S. E. Fehnel, and R. D. Hays, "Interpreting patient-reported outcome results: US FDA guidance and emerging methods," Expert review of pharmacoeconomics & outcomes research, vol. 11, no. 2, pp. 163-169, 2011. R. D. Hays, M. Brodsky, M. F. Johnston, K. L. Spritzer, and K.-K. Hui, "Evaluating the statistical significance of health-related quality-of-life change in individual patients," Evaluation & the Health Professions, vol. 28, no. 2, pp. 160-171, 2005. M. T. King, A. C. Dueck, and D. A. Revicki, "Can methods developed for interpreting group-level patient-reported outcome data be applied to individual patient management?," Medical care, vol. 57, no. Suppl 5 1, p. S38, 2019. N. S. Jacobson and P. Truax, "Clinical significance: A statistical approach to defining meaningful change in psychotherapy research," Journal of Consulting and Clinical Psychology, vol. 59, no. 1, pp. 12-19, 1991, doi: 10.1037/0022-006X.59.1.12. R. D. Hays, K. L. Spritzer, C. D. Sherbourne, G. W. Ryan, and I. D. Coulter, "Group and individual-level change on health-related quality of life in chiropractic patients with chronic low back or neck pain," Spine, vol. 44, no. 9, p. 647, 2019.

View all available languages for this measure. BACK FACT-BP Languages Chinese - Simplified Danish Dutch English French German Greek Hungarian Italian Japanese Polish Portuguese Russian Spanish Ukrainian

Committed to rigorous scientific measurement of the patient's perspective. FACIT.org Team COMMITTED TO RIGOROUS SCIENTIFIC MEASUREMENT OF THE PATIENT'S PERSPECTIVE Jason Bredle, MFA Director, FACIT.org BIO Shannon Romo Financial Manager BIO Sharon Debb, M.Ed Licensing Manager BIO Robert Martin, BA Licensing Support Specialist BIO Paul Cella, PhD Licensing Support Specialist BIO David Cella, PhD President, Chief Scientific Officer BIO Benjamin Arnold, MA President, FACITtrans, LLC BIO Lauren Lent, DHA, MS Founder, The FACIT Group BIO Luke Pickard, MBA Director of IT, Data Protection Officer (IT DPO) BIO Our History Over 25 years of improving patient health status measurement. LEARN MORE Our Vision, Mission, and Values At FACIT, our passion is the patient. Our mission is to provide a voice for patients worldwide. LEARN MORE

Download this measure in English. BACK FACT-M English Downloads Download PDF Download DOC

View all available languages for this measure. BACK FACT-CTCL Languages English

Scoring OF THE FACIT MEASURES For all FACIT measures, higher scores are better than lower scores. This is true whether measuring a symptom or a functional ability. All FACIT measures use raw total scoring approach without subsequent transformation. Scoring recommendations permit for a variety of component and composite calculations, depending on the desired outcome assessment, meeting FDA guidance recommendations for both global and targeted symptom evaluation. For any FACIT measure, subscale scores are calculated by first reversing negatively stated-items (subtracting the response from ‘4’) and then summing the raw (0-4) scores. A total score is then derived by summing subscale scores. For example, a total FACT-G score is obtained by summing individual subscale scores PWB + EWB + SWB + FWB. Total scores for the disease-, treatment-, and condition-specific subscales are typically obtained by summing all subscale scores PWB + EWB + SWB + FWB + additional concerns subscale. The scoring templates provided for each measure simplify this process by providing a framework to reverse score relevant items and prorate for missing data. TOI The TOI can be computed for any FACIT disease-, treatment-, or condition-specific scale. It is the sum of the Physical Well-Being (PWB), Functional Well-Being (FWB), and additional concerns subscales. Our experience with this TOI endpoint is that it is an efficient summary index of physical/functional outcomes. It is therefore a common endpoint used in clinical trials, because it is responsive to change in physical/functional outcomes, sometimes more than a total (overall) multidimensional aggregated score, which includes social and emotional well-being. While social and emotional well-being are very important to quality of life, they are not as likely to change as quickly or dramatically over time or in response to physical health interventions such as pharmaceutical treatments in clinical trials. Missing Data Relevant scoring options are outlined on each measure’s scoring template, where calculating reverse scored items and prorating for missing data has been integrated. In cases where individual items are skipped, subscale scores can be prorated using the average of the other answers in the scale. This is acceptable as long as more than 50% of the items were answered in the subscale (e.g., a minimum of 4 of 7 items, 4 of 6 items, etc.). The total score is then calculated as the sum of the un-weighted subscale scores. A FACIT measure is considered to be an acceptable indicator of patient quality of life as long as overall item response rate is greater than 80% (e.g., at least 22 of 27 FACT-G items completed). This is not to be confused with individual subscale item response rate, which allows a subscale score to be prorated for missing items if greater than 50% of items are answered. In addition, a total score should only be calculated if ALL of the component subscales have valid scores. Scoring is intended to be completed by research and clinical staff rather than patients themselves. Raw scoring templates are available in English and electronic scoring options are currently not available from FACIT.

View all available languages for this measure. BACK FACIT-GP5 Languages Afrikaans Albanian Amharic Arabic Armenian Assamese Belarusian Bengali Bosnian Bulgarian Burmese Catalan Cebuano Chinese - Simplified Chinese - Traditional Croatian Czech Danish Dutch English Estonian Farsi Finnish French Galician Georgian German Greek Gujarati Haitian Creole Hebrew Hiligaynon Hindi Hungarian Icelandic Ilokano Indonesian Italian Japanese Kannada Kazakh Korean Latvian Lithuanian Luganda Macedonian Malay Malayalam Maltese Marathi Mongolian Montenegrin Nepali Norwegian Odia Polish Portuguese Punjabi Romanian Russian Sepedi Serbian Sesotho Setswana Sinhala Slovak Slovene Spanish Swahili Swedish Tagalog Tamil Telugu Thai Tsonga Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Wolof Xhosa Zulu

Functional Assessment of Cancer Therapy - General FACT-G Caregiver Functional Assessment of Cancer Therapy - General - Caregiver Adaptation of FACT-G for family caregivers LICENSE THIS MEASURE Overview Language Availability Licensing Selected References Related Measures Overview Overview This version of the FACT-G has been adapted for family members caring for a loved one with cancer and can be scored like the FACT-G. MEASURE NAME: Functional Assessment of Cancer Therapy - General - Caregiver (FACT-G Caregiver) VERSION: 4 NUMBER OF ITEMS: 27 PATIENT POPULATION: Family caregivers 18 years and older caring for cancer patients RECALL PERIOD: Past 7 days RESPONSE SCALE: 5 point Likert-type scale DATA COLLECTION: Paper and electronic ADMINISTRATION: Self-administration and interview when applicable SUBSCALE DOMAINS: Physical Well-Being, Social/Family Well-Being, Emotional Well-Being, Functional Well-Being TIME FOR COMPLETION: 5-10 minutes SCORING: Manual scoring template, some items are reverse scored. Subscale scores and total scores possible. RELATED MEASURES: FACT-G , FACT-GP , CRRS DOWNLOAD MEASURE IN ENGLISH DOWNLOAD SCORING DOCUMENT Language Availability Available translations of the FACT-G Caregiver can be obtained by registering for permission. Users are not permitted to translate the FACT-G Caregiver without permission from FACIT.org. Permission from FACIT.org to translate the FACT-G Caregiver may also be contingent upon timeline expectations and availability of FACIT staff. Translations must undergo a rigorous methodology under the guidance of FACIT.org which includes multiple translators, QA steps and cognitive interviews with patients. For commercial use, FACITtrans is the approved translation vendor to translate the FACIT measurement system. Please contact us for more information. VIEW AVAILABLE LANGUAGES Language Availability Licensing Licensing Licensing fees are assessed on a per trial/per measure basis for commercial use. There is no fee for use of the English version, but a license should be obtained. Non-commercial use is assessed on a case-by-case basis. Licensing fees are typically not applied to investigator-initiated research, students, or clinical use. To license an available version of this measure for commercial or non-commercial use, please complete our registration form . All of the information provided in the form will be kept strictly confidential. For questions, please contact us . LICENSE THIS MEASURE Selected References Selected References Song L., Tan X., Bredle J., Bennett A.V., Northouse L. Psychometric properties of the FACT-G quality of life scale for family caregivers of cancer patients. Qual Life Res 2020 Aug;29(8):2241-2252. doi: 10.1007/s11136-020-02477-7. Epub 2020 Apr 3. PMID: 32246432; PMCID: PMC7363734. Northouse L., Kershaw T., Mood D., Schafenacker A. Effects of a family intervention on the quality of life of women with recurrent breast cancer and their family caregivers. Psychooncology 2005 Jun;14(6):478-91. doi: 10.1002/pon.871. PMID: 15599947. Northouse L.L., Mood D.W., Schafenacker A., Montie J.E., Sandler H.M., Forman J.D., Hussain M., Pienta K.J., Smith D.C., Kershaw T. Randomized clinical trial of a family intervention for prostate cancer patients and their spouses. Cancer 2007 Dec 15;110(12):2809-18. doi: 10.1002/cncr.23114. PMID: 17999405. Northouse L.L., Mood D.W., Schafenacker A., Kalemkerian G., Zalupski M., LoRusso P., Hayes D.F., Hussain M., Ruckdeschel J., Fendrick A.M., Trask P.C., Ronis D.L., Kershaw T. Randomized clinical trial of a brief and extensive dyadic intervention for advanced cancer patients and their family caregivers. Psychooncology 2013 Mar;22(3):555-63. doi: 10.1002/pon.3036. Epub 2012 Jan 31. PMID: 22290823; PMCID: PMC3387514. Bonomi, A.E., Cella, D.D., Hahn, E.A., Bjordal, K., Sperner, B., Gangeri, L., Bergman, B., Willems, J., Hanquet, P., & Zittoun, R. Multilingual translation of the Functional Assessment of Cancer Therapy (FACT) quality of life measurement system. Quality of Life Research 1996; 5: 309-320. Eremenco, S., Arnold, B., Cella, D. A comprehensive method for the translation and cross-cultural validation of health status questionnaires. Evaluation & the Health Professions 2005; 28(2): 212-232. Webster K., Cella D., Yost K. The Functional Assessment of Chronic Illness Therapy (FACIT) measurement system: Properties applications, and interpretation. Health and Quality of Life Outcomes 2003; 1(1): 79-85. Yost K.J., Eton D.T. Combining distribution- and anchor-based approaches to determine minimally important differences: The FACIT experience. Evaluation & the Health Professions 2005; 28(2): 172-191. LICENSE THIS MEASURE Related Measures Related Measures FACT-G Functional Assessment of Cancer Therapy - General LEARN MORE FACT-GP Functional Assessment of Cancer Therapy - General Population LEARN MORE CRRS Caregiver Roles and Responsibilities Scale LEARN MORE

Download this measure in English. BACK PROMIS Pool v1.0 - Dyspnea Task Avoidance English Downloads Download PDF Download DOC

Download this measure in English. BACK FAMS English Downloads Download PDF Download DOC

FACT-An Functional Assessment of Cancer Therapy – Anemia For patients with Anemia/Fatigue LICENSE THIS MEASURE Overview Language Availability Licensing Selected References Related Measures Overview Overview The Functional Assessment of Cancer Therapy – Anemia (FACT-An) contains 20 items related to symptoms and concerns of patients with anemia along with the 27-item Functional Assessment of Cancer Therapy-General (FACT-G). Thirteen of the 20 items deal with fatigue, while the remaining 7 cover other concerns related to anemia. Using semi-structured interviews with 14 anemic oncology patients and 5 oncology experts, two instruments were produced: The FACIT-Fatigue and the FACT-Anemia. These measures were then tested with a second sample of 50 cancer patients with hemoglobin levels ranging from 7 to 15.9 g/dL. The 47-item FAC7-An scores were found to be stable (test-retest r = 0.87) and internally consistent (coefficient alpha range= 0. 95-0. 96). The symptom-specific subscales also showed good stability (test-retest r range = 0.84-0.90), and the Fatigue subscale showed strong internal consistency (coefficient alpha range = 0. 93-0. 95). Internal consistency of the miscellaneous non-fatigue items was lower but acceptable (alpha range = 0.5 9-0. 70), particularly in light of their strong relationship to patient-rated performance status and hemoglobin level. Convergent and discriminant validity testing revealed a significant positive relationship with other known measures of fatigue, a significant negative relationship with vigor, and a predicted lack of relationship with social desirability. The FACT-An is useful measure of quality of life in cancer treatment, adding more focus to the problems of fatigue and anemia. MEASURE NAME: Functional Assessment of Cancer Therapy – Anemia (FACT-An) VERSION: 4 NUMBER OF ITEMS: 47 PATIENT POPULATION: Cancer patients 18 years and older with anemia RECALL PERIOD: Past 7 days RESPONSE SCALE: 5 point Likert-type scale DATA COLLECTION: Paper and electronic ADMINISTRATION: Self-administration and interview when applicable SUBSCALE DOMAINS: Physical Well-Being, Social/Family Well-Being, Emotional Well-Being, Functional Well-Being, Anemia Subscale TIME FOR COMPLETION: 10-15 minutes SCORING: Manual scoring template, some items are reverse scored. Subscale scores, total scores and TOI scores possible. SAS/SPSS algorithms available. RELATED MEASURES: FACIT-Fatigue , FACIT-F , PROMIS SF v1.0 – Fatigue 13a DOWNLOAD MEASURE IN ENGLISH DOWNLOAD SCORING DOCUMENT Language Availability Available translations of the FACT-An can be obtained by registering for permission. Users are not permitted to translate the FACT-An without permission from FACIT.org. Permission from FACIT.org to translate the FACT-An may also be contingent upon timeline expectations and availability of FACIT staff. Translations must undergo a rigorous methodology under the guidance of FACIT.org which includes multiple translators, QA steps and cognitive interviews with patients. For commercial use, FACITtrans is the approved translation vendor to translate the FACIT measurement system. Please contact us for more information. VIEW AVAILABLE LANGUAGES Language Availability Licensing Licensing Licensing fees are assessed on a per trial/per measure basis for commercial use. There is no fee for use of the English version, but a license should be obtained. Non-commercial use is assessed on a case-by-case basis. Licensing fees are typically not applied to investigator-initiated research, students, or clinical use. To license an available version of this measure for commercial or non-commercial use, please complete our registration form . All of the information provided in the form will be kept strictly confidential. For questions, please contact us . LICENSE THIS MEASURE Selected References Selected References Casadevall, N., Durieux, P., Dubois, S., Hemery, F., Lepage, E., Quarré, M., Damaj, G., Giraudier, S., Guerci, A., Laurent, G., Dombret, H., Chomienne, C., Ribrag, V., Stamatoullas, A., Marie, J., Vekhoff, A., Maloisel, F., Navarro, R., Dreyfus, F., & Fenaux, P. Health, economic, and quality-of-life effects of erythropoietin and granulocyte colony-stimulating factor for the treatment of myelodysplastic syndromes: a randomized, controlled trial. Blood 2004; 104(2): 321-327. doi: 10.1182/blood-2003-07-2252. Greenberg, P., Sun, Z., Miller, K., Bennett, J., Tallman, M., Dewald, G., van der Jagt, R., Houston, J., Thomas, M., Cella, D., & Rowe, J. Treatment of myelodysplastic syndromes patients with erythropoietin with or without granulocyte colony-stimulating factor: results of a prospective randomized phase III trial by the Eastern Cooperative Oncology Group (E1996). Blood 2009; 114(12): 2393–2400. doi: 10.1182/blood-2009-03-211797. Mesa, R., Niblack, J., Wadleigh, M., Verstovsek, S., Camoriano, J., Barnes, S., Tan, A., Atherton, P., Sloan, J., Tefferi, A. The Burden of Fatigue and Quality of Life in Myeloproliferative Disorders (MPDs). An International Internet-Based Survey of 1179 MPD Patients. Cancer 2007; 109(1): 68-76. doi: 10.1002/cncr.22365. Pinchon, D., Stanworth, S., Dore ´e, C., Brunskill, S., & Norfolk, D. Quality of life and use of red cell transfusion in patients with myelodysplastic syndromes. A systematic review. American Journal of Hematology 2009; 84(10):671-677. doi: 10.1002/ajh.21503. Spiriti. M., Latagliata, R., Niscola, P., Cortelezzi. A., Francesconi. M., Ferrari, D., Volpe, E., Clavio, M., Grossi. A., Reyes, M., Musto, P., Mitra, M., Azzarà, A., Pagnini, D., D’Arena, G., Spadano, A., Balleari, E., Pecorari, P., Capochiani, E., De Biasi, E., Perego, D., Monarca, B., Pisani, F., Scaramella, G., & Petti, M. Impact of a new dosing regimen of epoetin alfa on quality of life and anemia in patients with low-risk myelodysplastic syndrome. Ann Hematol 2005; 84: 167–176. doi: 10.1007/s00277-004-0961-9. Steensma, D., Heptinstall, K., Johnson, V., Novotny, P., Sloan, J., Camoriano, J., Niblack, J., Bennett, J., & Mesa, R. Common troublesome symptoms and their impact on quality of life in patients with myelodysplastic syndromes (MDS): Results of a large internet-based survey. Leukemia Research 2008; 32: 691-698. doi: 10.1016/j.leukres.2007.10.015. Trudeau, J.J., He, J., Rose, E. et al. Content validity of patient-reported outcomes for use in lower-risk myelodysplastic syndromes. J Patient Rep Outcomes 4, 69 (2020). doi.org/10.1186/s41687-020-00235-4. Yellen, S.B., Cella, D.F., Webster, K.A., Blendowski, C., & Kaplan, E. Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) Measurement System. Journal of Pain and Symptom Management 1997; 13(2): 63-74. Bonomi, A.E., Cella, D.D., Hahn, E.A., Bjordal, K., Sperner, B., Gangeri, L., Bergman, B., Willems, J., Hanquet, P., & Zittoun, R. Multilingual translation of the Functional Assessment of Cancer Therapy (FACT) quality of life measurement system. Quality of Life Research 1996; 5: 309-320. Eremenco, S., Arnold, B., Cella, D. A comprehensive method for the translation and cross-cultural validation of health status questionnaires. Evaluation & the Health Professions 2005; 28(2): 212-232. Webster K., Cella D., Yost K. The Functional Assessment of Chronic Illness Therapy (FACIT) measurement system: Properties applications, and interpretation. Health and Quality of Life Outcomes 2003; 1(1): 79-85. Yost K.J., Eton D.T. Combining distribution- and anchor-based approaches to determine minimally important differences: The FACIT experience. Evaluation & the Health Professions 2005; 28(2): 172-191. LICENSE THIS MEASURE Related Measures Related Measures FACIT-Fatigue Functional Assessment of Chronic Illness Therapy – Fatigue Scale LEARN MORE FACIT-F Functional Assessment of Chronic Illness Therapy – Fatigue LEARN MORE PROMIS SF v1.0 – Fatigue 13a Patient-Reported Outcomes Measurement Information System Short Form - Fatigue 13a LEARN MORE