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Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management

  • Mobile & Wireless Health
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Abstract

Poor patient compliance to therapy results in a worsening condition that often increases healthcare costs. In the MobiGuide project, we developed an evidence-based clinical decision-support system that delivered personalized reminders and recommendations to patients, helping to achieve higher therapy compliance. Yet compliance could still be improved and therefore building on the MobiGuide project experience, we designed a new component called the Motivational Patient Assistant (MPA) that is integrated within the MobiGuide architecture to further improve compliance. This component draws from psychological theories to provide behavioral support to improve patient engagement and thereby increasing patients’ compliance. Behavior modification interventions are delivered via mobile technology at patients’ home environments. Our approach was inspired by the IDEAS (Integrate, Design, Assess, and Share) framework for developing effective digital interventions to change health behavior; it goes beyond this approach by extending the Ideation phase’ concepts into concrete backend architectural components and graphical user-interface designs that implement behavioral interventions. We describe in detail our ideation approach and how it was applied to design the user interface of MPA for anticoagulation therapy for the atrial fibrillation patients. We report results of a preliminary evaluation involving patients and care providers that shows the potential usefulness of the MPA for improving compliance to anticoagulation therapy.

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References

  1. Centers for Disease Control and Prevention, Chronic disease overview, 2017. Available from: https://www.cdc.gov/chronicdisease/overview/index.htm.

  2. Brown, M. T., and Bussell, J. K., Medication adherence: WHO cares? Mayo Clin. Proc. 86(4):304–314, 2011.

    Article  Google Scholar 

  3. Chisholm-Burns, M., and Spivey, C., The “cost” of medication nonadherence: consequences we cannot afford to accept. J. Am. Pharm. Assoc. 52(6):823–826, 2012.

    Article  Google Scholar 

  4. Peleg, M., Shahar, Y., Quaglini, S., Fux, A., García-Sáez, G., Goldstein, A. et al., MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains. User Model User-Adapt Interact. 27(2):159–213, 2017.

    Article  Google Scholar 

  5. Peleg, M., Shahar, Y., Quaglini, S., Broens, T., Budasu, R., Fung, N. et al., Assessment of a personalized and distributed patient guidance system. Int. J. Med. Inform. 101:108–130, 2017 Available from: http://www.mobiguide-project.eu/images/IJMI.pdf.

    Article  Google Scholar 

  6. Greco, S., Matarazzo, B., and Slowinski, R., Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1):1–47, 2001.

    Article  Google Scholar 

  7. Wilk, S., O’Sullivan, D., Michalowski, M., Michalowski, W., Peleg, M., and Carrier, M., A data- and expert-driven decision support framework for helping patients adhere to therapy: psychobehavioral targets and associated interventions. In: Proceedings of the international joint workshop on Knowledge Representation for Health Care, Process-Oriented Information Systems in Health Care, Extraction and Processing of Rich Semantics from Medical Texts (KR4HC-ProHealth-RichMedSem 2017). p. 53–65, 2017.

  8. Schnall, R., Rojas, M., Bakke, S., Brown, W., Carballo-Dieguez, A., Carry, M. et al., A user-centered model for designing consumer mobile health (mHealth) applications. J. Biomed. Inform. 60:243–251, 2016.

    Article  Google Scholar 

  9. Collins, L. M., Murphy, S. A., Nair, V. N., and Strecher, V. J., A strategy for optimizing and evaluating behavioral interventions. Ann. Behav. Med. 30:65–73, 2005.

    Article  Google Scholar 

  10. Mummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D., and Sutton, S., IDEAS (integrate, design, assess, and share): a framework and toolkit of strategies for the development of more effective digital interventions to change health behavior. JMIR. 18(12):e317, 2016.

    PubMed  Google Scholar 

  11. Mummah, S. A., King, A. C., Gardner, C. D., and Sutton, S., Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption. Int. J. Behav. Nutr. Phys. Act. 8(13):90, 2016.

    Article  Google Scholar 

  12. Godino, J. G., Merchant, G., Norman, G. J., Donohue, M. C., Marshall, S. J., Fowle, J. H. et al., Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial. Lancet Diabetes Endocrinol. 4(9):747–755, 2016.

    Article  Google Scholar 

  13. Jin, J., Sklar, G. E., Min Sen Oh, V., and Li, S. C., Factors affecting therapeutic compliance: a review from the patient’s perspectiveo title. Ther. Clin. Risk Manag. 4(1):269–286, 2008.

    PubMed  PubMed Central  Google Scholar 

  14. Ream, M., Jacobs, J. M., Fishbein, J. N., Pensak, N., Nisotel, L. E., MacDonald, J. J., Buzaglo, J. S., Lennes, I. T., Safren, S. A., Pirl, W. F., Temel, J. S., and Greer, J., Patient engagement with a smartphone mobile app for adherence to oral chemotherapy. J. Clin. Oncol. 35(31_suppl):243, 2017.

    Article  Google Scholar 

  15. Graffigna, G., Barello, S., and Bonanomi, A., The role of patient health engagement model (PHE-model) in affecting patient activation and medication adherence: a structural equation model. PLoS One 12(6):e0179865, 2017.

    Article  Google Scholar 

  16. Müllerová, H., Landis, S. H., Aisanov, Z., Davis, K. J., Ichinose, M., Mannino, D. M. et al., Health behaviors and their correlates among participants in the continuing to confront COPD international patient survey. Int. J. Chron. Obs. Pulmon. Dis. 11:881–890, 2016.

    Google Scholar 

  17. Lynch, W., Perosino, K., and Slover, M., Altarum Institute Spring 2014 Survey of Consumer Health Care Opinions - Consumers in the Driver’s Seat, 2014. Available from: Altarum.org.

  18. Viswanathan, M., Golin, C., Jones, C., Ashok, M., Blalock, S., Wines, R. et al., Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review. Ann. Intern. Med. 157(11):785–795, 2012.

    Article  Google Scholar 

  19. Krukowski, R. A., Harvey-Berino, J., Bursac, Z., Ashikaga, T., and West, D. S., Patterns of success: online self-monitoring in a web-based behavioral weight control program. Health Psychol. 32(2):164–170, 2013.

    Article  Google Scholar 

  20. Smith, K. L., Kerr, D. A., Fenner, A. A., and Straker, L. M., Adolescents just do not know what they want: a qualitative study to describe obese adolescents’ experiences of text messaging to support behavior change maintenance post intervention. J. Med. Internet Res. 16(4):e103, 2014.

    Article  Google Scholar 

  21. Reinwand, D. A., Crutzen, R., Storm, V., Wienert, J., Kuhlmann, T., de Vries, H. et al., Generating and predicting high quality action plans to facilitate physical activity and fruit and vegetable consumption: results from an experimental arm of a randomised controlled trial. BMC Public Health 16:317, 2016.

    Article  Google Scholar 

  22. Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., and Mermelstein, R., Health behavior models in the age of mobile interventions: are our theories up to the task? Transl. Behav. Med. 1(1):53–71, 2011.

    Article  Google Scholar 

  23. Costa, E., Giardini, A., Savin, M., Menditto, E., Lehane, E., Laosa, O. et al., Interventional tools to improve medication adherence: review of literature. Patient Prefer Adherence. 9:1303–1314, 2015.

    Article  Google Scholar 

  24. Norcross, J. C., Krebs, P. M., and Prochaska, J. O., Stages of change. J. Clin. Psychol. 67(2):143–154, 2011.

    Article  Google Scholar 

  25. Ryan, R. M., and Deci, E. L., Self-determination theory : basic psychological needs in motivation, development, and wellness. Guilford Press: New York, 2017.

    Google Scholar 

  26. Fogg, B. J., A behavior model for persuasive design. In: Proceedings of the 4th international conference on persuasive technology. ACM, 2009.

  27. Abraham, C., and Michie, S., A taxonomy of behavior change techniques used in interventions. Health Psychol. 27(3):379–387, 2008.

    Article  Google Scholar 

  28. Gladstone, D. J., Bui, E., Fang, J., Laupacis, A., Lindsay, M. P., and Tu, J. V., Potentially preventable strokes in high-risk patients with atrial fibrillation who are not adequately anticoagulated. Stroke 40:235–240, 2009.

    Article  CAS  Google Scholar 

  29. Yao, X., Abraham, N. S., Alexander, F. C., Crown, W., Montori, V. M., Sangaralingham, L. R. et al., Effect of adherence to Oral anticoagulants on risk of stroke and major bleeding among patients with atrial fibrillation. J. Am. Heart Assoc. 5(2):e003074, 2016.

    Article  Google Scholar 

  30. Prochaska, J. O., and Prochaska, J. M., Changing to thrive: using the stages of change to overcome the top threats to your health and happiness. Hazelden Publishing: Center City, 2016.

    Google Scholar 

  31. Nielsen, J., 10 usability heuristics for user Interface design, 1995. Available from: https://www.nngroup.com/articles/ten-usability-heuristics/.

  32. Müller, S., Kohlmann, T., and Wilke, T., Validation of the adherence barriers questionnaire – an instrument for identifying potential risk factors associated with medication-related non-adherence. BMC Health Serv. Res. 15:153, 2015.

    Article  Google Scholar 

  33. Pollak, J. P., Adams, P., and Gay, G., PAM: a photographic affect meter for frequent, in situ measurement of affect. In: SIGCHI conference on Human factors in computing systems. p. 725–34, 2011.

  34. Apotex, Anticoagulation therapy personal medication record, 2008.

  35. Abu-Dalbouh, H. M., A questionnaire approach based on the technology acceptance model for mobile tracking on patient progress applications. J. Comput. Sci. 9(6):763–770, 2013.

    Article  Google Scholar 

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Acknowledgements

We thank the anonymous patients and Dr. LeGal from the Ottawa Hospital for assessing the MPA prototype for the AF domain. We thank Ofer Ben-Shachar, founder of healthoutcome.org, and Dr. Joel Lanir, head of the Human-computer Interactions Lab at the University of Haifa, for providing comments and suggestions for the design of the reporting and summary screens of the MPA prototype. We thank the students in our research groups for providing comments on the clarity of the earlier versions of the prototype and on the patient scenario questionnaire.

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Authors and Affiliations

  1. University of Haifa, Haifa, Israel

    Mor Peleg

  2. University of Ottawa, Ottawa, ON, Canada

    Wojtek Michalowski, Szymon Wilk, Enea Parimbelli & Silvia Bonaccio

  3. Poznan University of Technology, Poznan, Poland

    Szymon Wilk

  4. National College of Ireland, Dublin, Ireland

    Dympna O’Sullivan

  5. University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  6. University of Pavia, Pavia, Italy

    Silvana Quaglini

  7. The Ottawa Hospital Research Institute, Ottawa, ON, Canada

    Marc Carrier

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  1. Mor Peleg
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Correspondence to Mor Peleg.

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This article is part of the Topical Collection on Mobile & Wireless Health.

Appendix 1

Appendix 1

Table 7 A short interview with an AF patient from Italy who explains his view of how an engaged patient is managing AF condition
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Peleg, M., Michalowski, W., Wilk, S. et al. Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management. J Med Syst 42, 234 (2018). https://doi.org/10.1007/s10916-018-1077-4

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