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Bridging Patients’ Needs and Caregivers’ Perspectives to Tailor Information Provisioning during Cardiac Rehabilitation

1 , 2 , 3 , 1

Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

Human Computer Interaction Conference

4 - 6 July 2018

Patient-centered Computing, E-learning, Shared Decision Making, Cardiac Rehabilitation

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      Abstract

      In remote rehabilitation of cardiac patients, patients need a better understanding of various factors influencing their disease condition to become active participants in their care. Nonetheless, current e-learning approaches in healthcare lack personalization and a deeper understanding of individual patient needs. Most e-learning platforms in healthcare are merely an accumulation of content created by caregivers where patients have no means to seek tailored information to suit specific personal needs. This forms a barrier in patient understanding, debilitating them from becoming active stakeholders in their rehabilitation progress. We identify pitfalls in current approaches and gaps in information needs of patients and caregivers’ perspectives from literature. We organized two workshops - (i) with various professional caregivers involved in coaching cardiac patients, and (ii) with cardiac patients and their informal caregivers - to bridge caregivers’ perspectives with patients’ needs. Further, we prototyped and evaluated two tools to support shared decision making of information needs based on outcomes synthesized from the two workshops. In this paper, we discuss results of the workshops and prototype evaluations. Finally, we discuss how this shared decision making approach supports patient understanding and improves their adherence to rehabilitation goals.

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      Most cited references 27

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      The Law of Attrition

      In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.
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        Referral, enrollment, and delivery of cardiac rehabilitation/secondary prevention programs at clinical centers and beyond: a presidential advisory from the American Heart Association.

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          Shared decision making: Concepts, evidence, and practice

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            Author and article information

            Affiliations
            [1 ] Hasselt University-tUL-Expertise

            Center for Digital Media

            Wetenschapspark 2, 3590 Diepenbeek, Belgium
            [2 ] Hasselt University-tUL

            Martelarenlaan 42, 3500 Hasselt, Belgium
            [3 ] Hasselt University-Faculty of Medicine and Life Sciences; Department of Cardiology, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
            Contributors
            Conference
            July 2018
            July 2018
            : 1-11
            10.14236/ewic/HCI2018.49
            © Sankaran et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Proceedings of the 32nd International BCS Human Computer Interaction Conference
            HCI
            32
            Belfast, UK
            4 - 6 July 2018
            Electronic Workshops in Computing (eWiC)
            Human Computer Interaction Conference
            Product
            Product Information: 1477-9358 BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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