Guided or factual computer support for kidney patients with different experience levels and medical health situations: preferences and usage

  • Wenxin WangEmail author
  • Céline L. van Lint
  • Willem-Paul Brinkman
  • Ton J. M. Rövekamp
  • Sandra van Dijk
  • Paul van der Boog
  • Mark A. Neerincx
Original Paper


Personalization of eHealth systems is a promising technique for improving patients’ adherence. This paper explores the possibility of personalisation based on the patients’ medical health situation and on their health literacy. The study is set within the context of a self-management support system (SMSS) for renal transplant patients. A SMSS is designed with layering, nudging, emphaticizing, and focusing principles. It has two communication styles: (1) a guided style that provided more interpretation support and addressed emotional needs; and (2) a factual style that showed only measurement history, medical information, and recommendations. To evaluate the design, 49 renal transplant patients with three different experience levels participated in a lab study, in which they used the system in imaginary scenarios to deal with three medical health situations (alright, mild concern, and concern). A 96% understanding and 87% adherence rate was observed, with a significant interaction effect on adherence between patient group and health situation. Furthermore, compared to recently transplanted patients, not recently transplanted patients were relatively more positive towards the factual than the guided communication style in the “alright” condition. Furthermore, additional medical information was searched more often in health situations that causes mild concern and a majority of patients did not change the communication style to their preferred styles. By attuning the communication style to patient’s experience and medical health situation according to the applied principles and acquired insights, SMSSs are expected to be better used.


Self-management support system User interface Renal transplant patient Adherence Health literacy Explainable artificial intelligence 



As part of the ADMIRE project, this work is funded by the Netherlands Organisation for Health Research and Development (ZonMw, project no 300040004).

Compliance with Ethical Standards

Conflict of interest

All Authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.”


  1. 1.
    Hagger M, Orbell S. A meta-Analytic Review of the Common-Sense Model of Illness Representations. Psychol Health. 2003;18(2):141–84.CrossRefGoogle Scholar
  2. 2.
    Lorig KR, Holman HR. Self-management education: History, definition, outcomes, and mechanisms. Ann Behav Med. 2003;26(1):1–7.CrossRefGoogle Scholar
  3. 3.
    Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-Based Chronic Disease Self-Management: A Randomized Trial. Med Care. 2006;44(11):964–71.CrossRefGoogle Scholar
  4. 4.
    Frederix I, Hansen D, Coninx K, Vandervoort P, Vandijck D, Hens N, et al. Effect of comprehensive cardiac telerehabilitation on one-year cardiovascular rehospitalization rate, medical costs and quality of life: A cost-effectiveness analysis. Eur J Prev Cardiol. 2016;23(7):674–82.CrossRefGoogle Scholar
  5. 5.
    Eland-de Kok P, van Os-Medendorp H, Vergouwe-Meijer A, Bruijnzeel-Koomen C, Ros W. A systematic review of the effects of e-health on chronically ill patients. J Clin Nurs. 2011;20(21-22):2997–3010.CrossRefGoogle Scholar
  6. 6.
    Wildevuur S, Thomese F, Ferguson J, Klink A. Information and Communication Technologies to Support Chronic Disease Self-Management: Preconditions for Enhancing the Partnership in Person-Centered Care. J Participat Med. 2017;9(1):e14. Scholar
  7. 7.
    Dinesen B, Nonnecke B, Lindeman D, Toft E, Kidholm K, Jethwani K, et al. Personalized Telehealth in the Future: A Global Research Agenda. J Med Internet Res. 2016;18(3):e53. Scholar
  8. 8.
    Horsch C, Spruit S, Lancee J, van Eijk R, Beun RJ, Neerincx M, et al. Reminders make people adhere better to a self-help sleep intervention. Heal Technol. 2017;7(2):173–88. Scholar
  9. 9.
    Tielman ML, Neerincx MA, Brinkman WP. Design and evaluation of personalized motivational messages by a virtual agent that assists in post-traumatic stress disorder therapy. Journal of Medical Internet Research. 2017. Accepted.Google Scholar
  10. 10.
    Wang W, van Lint CL, Brinkman W-P, Rövekamp TJM, van Dijk S, van der Boog PJM, et al. Renal transplant patient acceptance of a self-management support system. BMC medical informatics and decision making. 2017;17(1):58. Scholar
  11. 11.
    van Lint C, Wang W, van Dijk S, Brinkman W-P, Rövekamp TJ, Neerincx MA, et al. Self-monitoring kidney function post transplantation: Reliability of patient-reported data. J Med Internet Res. 2017;19(9).Google Scholar
  12. 12.
    van Lint CL, van der Boog PJ, Romijn FP, Schenk PW, van Dijk S, Rövekamp TJ, et al. Application of a point of care creatinine device for trend monitoring in kidney transplant patients: fit for purpose? Clinical Chemistry and Laboratory Medicine (CCLM). 2015;53(10):1547–56.Google Scholar
  13. 13.
    van Lint CL, van der Boog PJ, Wang W, Brinkman W-P, Rövekamp TJ, Neerincx MA, et al. Patient experiences with self-monitoring renal function after renal transplantation: results from a single-center prospective pilot study. Patient Preference and Adherence. 2015;9:1721.CrossRefGoogle Scholar
  14. 14.
    Clark NM, Gong M, Kaciroti NA. Model of Self-Regulation for Control of Chronic Disease. Health Educ Behav. 2014;41(5):499–508.CrossRefGoogle Scholar
  15. 15.
    Axelrod R. Schema theory: An information processing model of perception and cognition. Am Polit Sci Rev. 1973;67(04):1248–66.CrossRefGoogle Scholar
  16. 16.
    Thaler RH, Sunstein CR. Nudge: improving decisions about health, wealth, and happiness. New Haven: Yale University Press; 2008.Google Scholar
  17. 17.
    Derksen F, Bensing J, Lagro-Janssen A. Effectiveness of empathy in general practice: a systematic review. Br J Gen Pract. 2013;63(606):e76–84.CrossRefGoogle Scholar
  18. 18.
    Reeves B, Nass C. How people treat computers, television, and new media like real people and places. Cambridge: CSLI Publications and Cambridge University Press; 1996.Google Scholar
  19. 19.
    Fogg BJ. Persuasive technology: using computers to change what we think and do. Burlington: Morgan Kaufmann Publishers; 2003.CrossRefGoogle Scholar
  20. 20.
    Blanson Henkemans OA, van der Boog PJM, Lindenberg J, van der Mast CAPG, Neerincx MA, Zwetsloot-Schonk BJHM. An online lifestyle diary with a persuasive computer assistant providing feedback on self-management. Technol Health Care. 2009;17(3):253–67.Google Scholar
  21. 21.
    Karvonen K. The beauty of simplicity. Proceedings on the 2000 conference on Universal Usability; Arlington. 355478: ACM; 2000. p. 85-90.Google Scholar
  22. 22.
    Baines LS, Joseph JT, Jindal RM. Emotional issues after kidney transplantation: a prospective psychotherapeutic study. Clin Transpl. 2002;16(6):455–60.CrossRefGoogle Scholar
  23. 23.
    Diamond DM, Campbell AM, Park CR, Halonen J, Zoladz PR. The temporal dynamics model of emotional memory processing: a synthesis on the neurobiological basis of stress-induced amnesia, flashbulb and traumatic memories, and the Yerkes-Dodson law. Neural plasticity. 2007;2007.Google Scholar
  24. 24.
    Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clinic Proceedings; 2011: Elsevier.Google Scholar
  25. 25.
    Heller RF, Rose G, Pedoe HD, Christie DG. Blood pressure measurement in the United Kingdom Heart Disease Prevention Project. J Epidemiol Community Health. 1978;32(4):235–8.CrossRefGoogle Scholar
  26. 26.
    Sulbaran T, Silva E, Calmon G, Vegas A. Epidemiologic aspects of arterial hypertension in Maracaibo, Venezuela. J Hum Hypertens. 2000;14:S6–9.CrossRefGoogle Scholar
  27. 27.
    Stock C, Holleczek B, Hoffmeister M, Stolz T, Stegmaier C, Brenner H. Adherence to Physician Recommendations for Surveillance in Opportunistic Colorectal Cancer Screening: The Necessity of Organized Surveillance. PLoS One. 2013;8(12):e82676.CrossRefGoogle Scholar
  28. 28.
    Hudson SV, Ferrante JM, Ohman-Strickland P, Hahn KA, Shaw EK, Hemler J, et al. Physician recommendation and patient adherence for colorectal cancer screening. The Journal of the American Board of Family Medicine. 2012;25(6):782–91.CrossRefGoogle Scholar
  29. 29.
    Sabaté E, Organization WH. Adherence to Long-term Therapies: Evidence for Action. Geneva: World Health Organization; 2003.Google Scholar
  30. 30.
    Karter AJ, Ferrara A, Darbinian JA, Ackerson LM, Selby JV. Self-monitoring of blood glucose: language and financial barriers in a managed care population with diabetes. Diabetes Care. 2000;23(4):477–83.CrossRefGoogle Scholar
  31. 31.
    Shobhana R, Begum R, Snehalatha C, Vijay V, Ramachandran A. Patients' adherence to diabetes treatment. J Assoc Physicians India. 1999;47(12):1173–5.Google Scholar
  32. 32.
    Horsch C, Lancee J, Beun RJ, Neerincx MA, Brinkman W-P. Adherence to Technology-Mediated Insomnia Treatment: A Meta-Analysis, Interviews, and Focus Groups. J Med Internet Res. 2015;17(9):e214.CrossRefGoogle Scholar
  33. 33.
    Medlock S, Eslami S, Askari M, Arts DL, Sent D, de Rooij SE, et al. Health Information–Seeking Behavior of Seniors Who Use the Internet: A Survey. J Med Internet Res. 2015;17(1).Google Scholar
  34. 34.
    Kahneman D. A perspective on judgment and choice: mapping bounded rationality. Am Psychol. 2003;58(9):697.CrossRefGoogle Scholar
  35. 35.
    Agarwal R, Anderson C, Zarate J, Ward C. If We Offer it, Will They Accept? Factors Affecting Patient Use Intentions of Personal Health Records and Secure Messaging. J Med Internet Res. 2013;15(2):e43.CrossRefGoogle Scholar
  36. 36.
    Rosenstock IM, Strecher VJ, Becker MH. Social learning theory and the health belief model. Health Educ Behav. 1988;15(2):175–83.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Intelligent SystemsDelft University of TechnologyDelftthe Netherlands
  2. 2.TNOthe Haguethe Netherlands
  3. 3.Department of NephrologyLeiden University Medical CenterLeidenthe Netherlands
  4. 4.Department of Social and Behavioral Sciences, Health, Medical and Neuropsychology UnitLeiden UniversityLeidenthe Netherlands

Personalised recommendations