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Presentation of Personal Health Information for Consumers: An Experimental Comparison of Four Visualization Formats

  • Da Tao
  • Juan Yuan
  • Xingda Qu
  • Tieyan Wang
  • Xingyu Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)

Abstract

While the development of consumer-oriented health information technologies (CHITs) has led to increased availability and accessibility of personal health information, consumers may encounter difficulty in comprehending the information, partly due to inappropriate information presentation. This study was conducted to compare four visualization formats of personal health information in consumers’ use and comprehension of the information. A within-subjects design was employed, with visualization format serving as independent variable, and sets of user performance, perception, eye movement and preference measures serving as dependent variables. Twenty-four participants were recruited in this study. The results indicated that there was no significant main effect of visualization format on task completion time and accuracy rate, while visualization format yielded a significant effect on perceived health risk, perceived ease of understanding, perceived usefulness, perceived confidence of comprehension, and satisfaction. Participants’ visual attention, indicated by eye movement measures, was significantly affected by areas of interest, but not by visualization format. Most participants preferred personalized enhanced format. Our study demonstrates that visualization formats could affect how personal health information are comprehended and perceived. The results may help to improve the design of more usable and effective health information presentation.

Keywords

Visualization format Health information Comprehension Presentation 

References

  1. 1.
    National Health and Family Planning Commission of the PRC: Report on the status of Chinese residents’ nutrition and chronic diseases (2015). http://www.nhfpc.gov.cn/jkj/pgzdt/new_list_9.shtml
  2. 2.
    Pan, J.H., Shan, J.J.: Annual Report on Urban Development of China-No. 9. Social Science Academic Press, Beijing (2016)Google Scholar
  3. 3.
    The State Council of the People’s Republic of China: National population development plan (2016–2030 year) (2017). http://www.gov.cn/zhengce/content/2017-01/25/content_5163309.htm
  4. 4.
    Tao, D., Wang, T., Wang, T., Liu, S., Qu, X.: Effects of consumer-oriented health information technologies in diabetes management over time: a systematic review and meta-analysis of randomized controlled trials. J. Am. Med. Inform. Assoc. 24(5), 1014–1023 (2017).  https://doi.org/10.1093/jamia/ocx014CrossRefGoogle Scholar
  5. 5.
    Or, C.K.L., Tao, D.: Does the use of consumer health information technology improve outcomes in the patient self-management of diabetes? a meta-analysis and narrative review of randomized controlled trials. Int. J. Med. Inform. 83, 320–329 (2014).  https://doi.org/10.1016/j.ijmedinf.2014.01.009CrossRefGoogle Scholar
  6. 6.
    Tao, D., Shao, F., Liu, S., Wang, T., Qu, X.: Predicting factors of consumer acceptance of health information technologies: a systematic review. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. vol. 60(1), pp. 598–602 (2016)CrossRefGoogle Scholar
  7. 7.
    Or, C., Tao, D.: A 3-month randomized controlled pilot trial of a patient-centered, computer-based self-monitoring system for the care of type 2 diabetes mellitus and hypertension. J. Med. Syst. 40(4), 81 (2016).  https://doi.org/10.1007/s10916-016-0437-1CrossRefGoogle Scholar
  8. 8.
    Tao, D., Or, C.K.: Effects of self-management health information technology on glycaemic control for patients with diabetes: a meta-analysis of randomized controlled trials. J. Telemed. Telecare. 19, 133–143 (2013).  https://doi.org/10.1177/1357633x13479701CrossRefGoogle Scholar
  9. 9.
    Tao, D., Xie, L.Y., Wang, T.Y., Wang, T.S.: A meta-analysis of the use of electronic reminders for patient adherence to medication in chronic disease care. J. Telemed. Telecare 21(1), 3–13 (2015)CrossRefGoogle Scholar
  10. 10.
    Brewer, N.T., Gilkey, M.B., Lillie, S.E., Hesse, B.W., Sheridan, S.L.: Tables or bar graphs? Presenting test results in electronic medical records. Med. Decis. Making 32(4), 545–553 (2012).  https://doi.org/10.1177/0272989x12441395CrossRefGoogle Scholar
  11. 11.
    Torsvik, T., Lillebo, B., Mikkelsen, G.: Presentation of clinical laboratory results: an experimental comparison of four visualization techniques. J. Am. Med. Inform. Assoc. 20(2), 325–331 (2013).  https://doi.org/10.1136/amiajnl-2012-001147CrossRefGoogle Scholar
  12. 12.
    Jimison, H., Gorman, P., Woods, S., Nygren, P., Walker, M., Norris, S., et al.: Barriers and drivers of health information technology use for the elderly, chronically ill, and underserved. Evidence Report/Technology Assessment No. 175 (Prepared by the Oregon Evidence-based Practice Center under Contract No. 290-02-0024). AHRQ Publication No. 09-E004. Rockville, Agency for Healthcare Research and Quality (2008)Google Scholar
  13. 13.
    Or, C.K.L., Tao, D.: Usability study of a computer-based self-management system for older adults with chronic diseases. JMIR. Res. Protoc. 1, e13 (2012)CrossRefGoogle Scholar
  14. 14.
    Tao, D., Or, C. (eds.) A Paper Prototype Usability Study of a Chronic Disease Self-management System for Older Adults. 2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 10–13 Dec 2012, Hong Kong (2012)Google Scholar
  15. 15.
    Middleton, B., Bloomrosen, M., Dente, M.A., Hashmat, B., Koppel, R., Overhage, J.M., et al.: Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J. Am. Med. Inform. Assoc. 20(e1), e2–e8 (2013)CrossRefGoogle Scholar
  16. 16.
    Trevena, L.J., Zikmund-Fisher, B.J., Edwards, A., Gaissmaier, W., Galesic, M., Han, P.K., et al.: Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers. BMC Med. Inform. Decis. Mak. 13(Suppl 2), S7 (2013).  https://doi.org/10.1186/1472-6947-13-s2-s7CrossRefGoogle Scholar
  17. 17.
    Zikmund-Fisher, B.J., Exe, N.L., Witteman, H.O.: Numeracy and literacy independently predict patients’ ability to identify out-of-range test results. J Med Internet Res. 16, e187 (2014)CrossRefGoogle Scholar
  18. 18.
    Smith, S.G., Curtis, L.M., O’Conor, R., Federman, A.D., Wolf, M.S.: ABCs or 123 s? The independent contributions of literacy and numeracy skills on health task performance among older adults. Patient Educ. Couns. 98(8), 991–997 (2015)CrossRefGoogle Scholar
  19. 19.
    Galesic, M., Garcia-Retamero, R.: Statistical numeracy for health: a cross-cultural comparison with probabilistic national samples. Arch. Intern. Med. 170(5), 462–468 (2010).  https://doi.org/10.1001/archinternmed.2009.481CrossRefGoogle Scholar
  20. 20.
    Serper, M., Patzer, R.E., Curtis, L.M., Smith, S.G., O’Conor, R., Baker, D.W., et al.: Health literacy, cognitive ability, and functional health status among older adults. Health Serv. Res. 49(4), 1249–1267 (2014).  https://doi.org/10.1111/1475-6773.12154CrossRefGoogle Scholar
  21. 21.
    Taha, J., Sharit, J., Czaja, S.J.: The impact of numeracy ability and technology skills on older adults’ performance of health management tasks using a patient portal. J. Appl. Gerontol. 33(4), 416–436 (2014)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Norman, D.A.: Representations in distributed cognitive tasks. Cogn. Sci. 18(1), 87–122 (1994)CrossRefGoogle Scholar
  23. 23.
    Kurtzman, E.T., Greene, J.: Effective presentation of health care performance information for consumer decision making: A systematic review. Patient Educ. Couns. 99(1), 36–43 (2016).  https://doi.org/10.1016/j.pec.2015.07.030CrossRefGoogle Scholar
  24. 24.
    Hildon, Z., Allwood, D., Black, N.: Impact of format and content of visual display of data on comprehension, choice and preference: a systematic review. Int. J. Qual. Health Care 24(1), 55–64 (2012).  https://doi.org/10.1093/intqhc/mzr072CrossRefGoogle Scholar
  25. 25.
    Timmermans, D.R., Ockhuysen-Vermey, C.F., Henneman, L.: Presenting health risk information in different formats: the effect on participants’ cognitive and emotional evaluation and decisions. Patient Educ. Couns. 73(3), 443–447 (2008).  https://doi.org/10.1016/j.pec.2008.07.013CrossRefGoogle Scholar
  26. 26.
    Okan, Y., Stone, E.R., Bruine de Bruin, W.: Designing graphs that promote both risk understanding and behavior change. Risk Anal. (2017).  https://doi.org/10.1111/risa.12895CrossRefGoogle Scholar
  27. 27.
    Harris, R., Noble, C., Lowers, V.: Does information form matter when giving tailored risk information to patients in clinical settings? A review of patients’ preferences and responses. Patient Prefer Adherence 11, 389–400 (2017).  https://doi.org/10.2147/ppa.s125613CrossRefGoogle Scholar
  28. 28.
    Okan, Y., Galesic, M., Garcia-Retamero, R.: How people with low and high graph literacy process health graphs: evidence from eye-tracking. J. Behav. Decis. Mak. 29(2–3), 271–294 (2016).  https://doi.org/10.1002/bdm.1891CrossRefGoogle Scholar
  29. 29.
    Tao, D., Yuan, J., Liu, S., Qu, X.: Effects of button design characteristics on performance and perceptions of touchscreen use. Int. J. Ind. Ergon. 64, 59–68 (2018).  https://doi.org/10.1016/j.ergon.2017.12.001CrossRefGoogle Scholar
  30. 30.
    Garcia-Retamero, R., Cokely, E.T.: Communicating health risks with visual aids. Curr. Dir. Psychol. Sci. 22(5), 392–399 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Da Tao
    • 1
  • Juan Yuan
    • 1
  • Xingda Qu
    • 1
  • Tieyan Wang
    • 1
  • Xingyu Chen
    • 2
  1. 1.Institute of Human Factors and ErgonomicsShenzhen UniversityShenzhenChina
  2. 2.Department of MarketingShenzhen UniversityShenzhenChina

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