Understandability of Patient Information Booklets for Patients with Cancer

  • Christian Keinki
  • Richard Zowalla
  • Martin Wiesner
  • Marie Jolin Koester
  • Jutta Huebner


The improvement of health literacy in general and the information of individual patient is a major concern of the German national cancer plan and similar initiatives in other western countries. The aim of our study was to assess the readability and understandability of information booklets for cancer patients available at German Web sites. A support vector machine (SVM) was used to discriminate between laymen- and expert-centric patient information booklets about nine most common tumor types. All booklets had to be available for free at the Internet. A total of 52 different patient booklets were downloaded and assessed. Overall, the assessment of all booklets showed that an understandability level L of 4.6 and therefore increased medical background knowledge is required to understand a random text selected from the sample. The assessed information booklets on cancer show very limited suitability for laymen. We were able to demonstrate that a medical background is necessary to understand the examined booklets. The current study highlights the need to create information material adjusted to the needs of laymen. Assessing understandability before publication, especially for laymen with low health literacy, could ensure the suitability and thus quality of the information material.


Evidence-based health information Patient information booklets Understandability Readability Health literacy Oncology 


Compliance with Ethical Standards

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.


  1. 1.
    Hoefert H-W (2011) Wandel der Patientenrolle: neue Interaktionsformen im Gesundheitswesen [change of the patient role: new forms of interaction in healthcare]. Hogrefe, GöttingenGoogle Scholar
  2. 2.
    Berkman ND, Sheridan SL, Donahue KE et al (2011) Low health literacy and health outcomes: an updated systematic review. Ann Intern Med 155:97–107. doi: 10.7326/0003-4819-155-2-201107190-00005 CrossRefPubMedGoogle Scholar
  3. 3.
    Bundesministerium für Gesundheit (2012) Nationaler Krebsplan - Handlungsfelder, Ziele und Umsetzungsempfehlungen [National Cancer Plan - fields of action, objectives and implementation of recommendations]. Druckerei im Bundesministerium für Arbeit und Soziales, BerlinGoogle Scholar
  4. 4.
    Klemperer D, Lang B, Koch K et al (2010) Die ‚Gute praxis Gesundheitsinformation’ [the, good practice health information ’]. Z Für Evidenz Fortbild Qual Im Gesundheitswesen 104:66–68. doi: 10.1016/j.zefq.2009.12.018 CrossRefGoogle Scholar
  5. 5.
    Böcken J, Braun B, Landmann J (2010) Gesundheitsmonitor 2010 - Bürgerorientierung im Gesundheitswesen [health monitor 2010 - focus on citizens healthcare]. Bertelsmann Stiftung, GüterslohGoogle Scholar
  6. 6.
    Garcia SF, Hahn EA, Jacobs EA (2010) Addressing low literacy and health literacy in clinical oncology practice. J Support Oncol 8:64–69PubMedPubMedCentralGoogle Scholar
  7. 7.
    Kane HL, Halpern MT, Squiers LB et al (2014) Implementing and evaluating shared decision making in oncology practice. CA Cancer J Clin 64:377–388. doi: 10.3322/caac.21245 CrossRefPubMedGoogle Scholar
  8. 8.
    Keinki C, Seilacher E, Ebel M et al (2015) Information needs of cancer patients and perception of impact of the disease, of self-efficacy, and locus of control. J Cancer Educ Off J Am Assoc Cancer Educ. doi: 10.1007/s13187-015-0860-x Google Scholar
  9. 9.
    Huebner J, Micke O, Muecke R et al (2014) User rate of complementary and alternative medicine (CAM) of patients visiting a counseling facility for CAM of a German comprehensive cancer center. Anticancer Res 34:943–948PubMedGoogle Scholar
  10. 10.
    Friedman DB, Hoffman-Goetz L (2006) A systematic review of readability and comprehension instruments used for print and web-based cancer information. Health Educ Behav Off Publ Soc Public Health Educ 33:352–373. doi: 10.1177/1090198105277329 CrossRefGoogle Scholar
  11. 11.
    Estey A, Musseau A, Keehn L (1991) Comprehension levels of patients reading health information. Patient Educ Couns 18:165–169. doi: 10.1016/0738-3991(91)90008-S CrossRefGoogle Scholar
  12. 12.
    The Free Readability Test Tool - Readable. http://www.webpagefx.com/tools/read-able/. Accessed 7 May 2016
  13. 13.
    Charnock D, Shepperd S, Needham G, Gann R (1999) DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. J Epidemiol Community Health 53:105–111CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Yan X, Song D, Li X (2006) Concept-based Document Readability in Domain Specific Information Retrieval. In: Proc. 15th ACM Int. Conf. Inf. Knowl. Manag. ACM, New York, NY, USA, pp 540–549Google Scholar
  15. 15.
    Ownby RL (2005) Influence of vocabulary and sentence complexity and passive voice on the readability of consumer-oriented mental health information on the Internet. AMIA Annu Symp Proc AMIA Symp AMIA Symp 585–589Google Scholar
  16. 16.
    Leroy G, Miller T, Rosemblat G, Browne A (2008) A balanced approach to health information evaluation: a vocabulary-based naïve Bayes classifier and readability formulas. J Am Soc Inf Sci Technol 59:1409–1419. doi: 10.1002/asi.20837 CrossRefGoogle Scholar
  17. 17.
    Schmidt H, Alilovic I, Klärs G (2011) Evaluation schriftlicher Gesundheitsinformationen zu Brustkrebs [Evaluation of written health information about breast cancer]. Gesundheitsinformationen Dtschl. Eine Übers. Zu Anforderungen Angeboten HerausforderungenGoogle Scholar
  18. 18.
    Robert Koch-Institut, die Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V (2013) Krebs in Deutschland 2009/2010 [cancer in Germany 2009/2010]. Robert Koch-Institut, BerlinGoogle Scholar
  19. 19.
    Liebl P, Seilacher E, Koester M-J et al (2015) What cancer patients find in the internet: the visibility of evidence-based patient information - analysis of information on German websites. Oncol Res Treat 38:212–218. doi: 10.1159/000381739 CrossRefPubMedGoogle Scholar
  20. 20.
    Deutsche Krebshilfe (2016) Stiftung Deutsche Krebshilfe [German Cancer Aid Foundation]. http://www.krebshilfe.de/nc/startseite.html. Accessed 7 May 2016
  21. 21.
    GKV-Spitzenverband Krankenkassenliste - GKV-Spitzenverband [Health insurance list - GKV Head Association]. https://www.gkv-spitzenverband.de/service/versicherten_service/krankenkassenliste/krankenkassen.jsp. Accessed 7 May 2016
  22. 22.
    Products VBC, AG DT Umsatz und Forschungsausgaben der Top 50 Pharmaunternehmen weltweit im Jahr 2013 | Statistik [Sales and research expenditures of the top 50 pharmaceutical companies worldwide in 2013 | statistics]. In: Statista. http://de.statista.com/statistik/daten/studie/304720/umfrage/top-50-pharmaunternehmen-umsatz-und-forschungsausgaben/. Accessed 7 May 2016
  23. 23.
    Zowalla R, Wiesner M, Pfeifer D (2014) Automatically assessing the expert degree of online health content using SVMs. Stud Health Technol Inform 202:48–51PubMedGoogle Scholar
  24. 24.
    Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Mach. Learn. ECML-98. Springer Berlin, Heidelberg, pp. 137–142CrossRefGoogle Scholar
  25. 25.
    Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27. doi: 10.1145/1961189.1961199 CrossRefGoogle Scholar
  26. 26.
    Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Proc. Fourteenth Int. Conf. Mach. Learn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 412–420Google Scholar
  27. 27.
    Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24:513–523. doi: 10.1016/0306-4573(88)90021-0 CrossRefGoogle Scholar
  28. 28.
    Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10:61–74Google Scholar
  29. 29.
    Weintraub D, Maliski SL, Fink A et al (2004) Suitability of prostate cancer education materials: applying a standardized assessment tool to currently available materials. Patient Educ Couns 55:275–280. doi: 10.1016/j.pec.2003.10.003 CrossRefPubMedGoogle Scholar
  30. 30.
    Cox N, Bowmer C, Ring A (2011) Health literacy and the provision of information to women with breast cancer. Clin Oncol R Coll Radiol G B 23:223–227. doi: 10.1016/j.clon.2010.11.010 CrossRefGoogle Scholar
  31. 31.
    Singh J (2003) Reading grade level and readability of printed cancer education materials. Oncol Nurs Forum 30:867–870. doi: 10.1188/03.ONF.867-870 CrossRefPubMedGoogle Scholar
  32. 32.
    van Weert JCM, van Noort G, Bol N et al (2011) Tailored information for cancer patients on the internet: effects of visual cues and language complexity on information recall and satisfaction. Patient Educ Couns 84:368–378. doi: 10.1016/j.pec.2011.04.006 CrossRefPubMedGoogle Scholar
  33. 33.
    Knapp P, Gardner PH, Carrigan N et al (2009) Perceived risk of medicine side effects in users of a patient information website: a study of the use of verbal descriptors, percentages and natural frequencies. Br J Health Psychol 14:579–594. doi: 10.1348/135910708X375344 CrossRefPubMedGoogle Scholar
  34. 34.
    Seidel DG, Münch I, Dreier M et al (2014) Sind Informationsmaterialien zur Darmkrebsfrüherkennung in Deutschland verständlich oder verfehlen sie ihre Wirkung? Bundesgesundheitsbl Gesundheitsforsch Gesundheitsschutz 57:366–379. doi: 10.1007/s00103-013-1908-x CrossRefGoogle Scholar
  35. 35.
    Sheridan SL, Sutkowi-Hemstreet A, Barclay C et al (2016) A comparative effectiveness trial of alternate formats for presenting benefits and harms information for low-value screening services: a randomized clinical trial. JAMA Intern Med 176:31. doi: 10.1001/jamainternmed.2015.7339 CrossRefPubMedGoogle Scholar
  36. 36.
    Garcia-Retamero R, Galesic M (2010) Who profits from visual aids: overcoming challenges in people’s understanding of risks [corrected]. Soc Sci Med 1982 70:1019–1025. doi: 10.1016/j.socscimed.2009.11.031 Google Scholar
  37. 37.
    Brotherstone H, Miles A, Robb KA et al (2006) The impact of illustrations on public understanding of the aim of cancer screening. Patient Educ Couns 63:328–335. doi: 10.1016/j.pec.2006.03.016 CrossRefPubMedGoogle Scholar
  38. 38.
    Brundage M, Feldman-Stewart D, Leis A et al (2005) Communicating quality of life information to cancer patients: a study of six presentation formats. J Clin Oncol Off J Am Soc Clin Oncol 23:6949–6956. doi: 10.1200/JCO.2005.12.514 CrossRefGoogle Scholar
  39. 39.
    Lipkus IM, Hollands JG (1999) The visual communication of risk. J Natl Cancer Inst Monogr:149–163Google Scholar
  40. 40.
    Edwards A, Elwyn G, Mulley A (2002) Explaining risks: turning numerical data into meaningful pictures. BMJ 324:827–830CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Galesic M, Garcia-Retamero R, Gigerenzer G (2009) Using icon arrays to communicate medical risks: overcoming low numeracy. Health Psychol Off J Div Health Psychol Am Psychol Assoc 28:210–216. doi: 10.1037/a0014474 Google Scholar
  42. 42.
    Gaissmaier W, Wegwarth O, Skopec D et al (2012) Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information. Health Psychol Off J Div Health Psychol Am Psychol Assoc 31:286–296. doi: 10.1037/a0024850 Google Scholar
  43. 43.
    Schwartz LM, Woloshin S, Welch HG (2009) Using a drug facts box to communicate drug benefits and HarmsTwo randomized trials. Ann Intern Med 150:516–527. doi: 10.7326/0003-4819-150-8-200904210-00106 CrossRefPubMedGoogle Scholar
  44. 44.
    Woloshin S, Schwartz LM (2011) Communicating data about the benefits and harms of treatment: a randomized trial. Ann Intern Med 155:87–96. doi: 10.7326/0003-4819-155-2-201107190-00004 CrossRefPubMedGoogle Scholar
  45. 45.
    Stacey D, Légaré F, Col NF, et al (2014) Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev CD001431. doi:10.1002/14651858.CD001431.pub4Google Scholar
  46. 46.
    Spiegle G, Al-Sukhni E, Schmocker S et al (2013) Patient decision aids for cancer treatment: are there any alternatives? Cancer 119:189–200. doi: 10.1002/cncr.27641 CrossRefPubMedGoogle Scholar
  47. 47.
    Nannenga MR, Montori VM, Weymiller AJ et al (2009) A treatment decision aid may increase patient trust in the diabetes specialist. the Statin choice randomized trial. Health Expect Int J Public Particip Health Care Health Policy 12:38–44. doi: 10.1111/j.1369-7625.2008.00521.x Google Scholar
  48. 48.
    Edwards A, Gray J, Clarke A et al (2008) Interventions to improve risk communication in clinical genetics: systematic review. Patient Educ Couns 71:4–25. doi: 10.1016/j.pec.2007.11.026 CrossRefPubMedGoogle Scholar
  49. 49.
    Thorne SE, Bultz BD, Baile WF, SCRN Communication Team (2005) Is there a cost to poor communication in cancer care?: a critical review of the literature. Psychooncology 14:875–884. doi: 10.1002/pon.947 CrossRefPubMedGoogle Scholar
  50. 50.
    Holmes-Rovner M, Stableford S, Fagerlin A et al (2005) Evidence-based patient choice: a prostate cancer decision aid in plain language. BMC Med Inform Decis Mak 5:16. doi: 10.1186/1472-6947-5-16 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© American Association for Cancer Education 2016

Authors and Affiliations

  • Christian Keinki
    • 1
  • Richard Zowalla
    • 2
  • Martin Wiesner
    • 2
  • Marie Jolin Koester
    • 1
  • Jutta Huebner
    • 1
  1. 1.Working Group Integrative Oncology, Dr. Senckenberg Chronomedical InstituteJ.W. Goethe UniversityFrankfurtGermany
  2. 2.Department of Medical InformaticsHochschule HeilbronnHeilbronnGermany

Personalised recommendations