Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management
- 40 Downloads
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.
KeywordsPatient engagement Atrial fibrillation Mobile health Trans-theoretical model
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.
- 1.Centers for Disease Control and Prevention, Chronic disease overview, 2017. Available from: https://www.cdc.gov/chronicdisease/overview/index.htm.
- 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.CrossRefGoogle 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.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.CrossRefGoogle 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.CrossRefGoogle 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.
- 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.CrossRefGoogle 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.CrossRefGoogle 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.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/.
- 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.Google Scholar
- 34.Apotex, Anticoagulation therapy personal medication record, 2008.Google Scholar