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.
The six-minute walk test (6MWT) provides an objective measurement of a person’s functional exercise capacity. In this study, we developed a smartphone application that allows cardiac patients to do a self-administered 6MWT at home on a random trajectory. In a prospective study with 102 cardiovascular disease patients, we aimed to identify the optimal circumstances to perform a smartphone-measured 6MWT, i.e., the best algorithm and the best position to wear the smartphone during the test. Furthermore, we investigated if a random walk is as accurate as a standardized 6MWT. When considering both the reliability and accuracy of the distance walked, the best circumstances to perform a standardized smartphone-measured 6MWT are wearing the smartphone in a strap around the patient’s arm and using an algorithm that relies on the processed step count data acquired from Google Fit. Furthermore, we demonstrated that a smartphone-measured walk along a random trajectory is as accurate to determine a cardiac patient’s functional exercise capacity as a standardized (smartphone-measured) 6MWT. We conclude this paper by presenting how our 6MWT application can be used in a home setting to remotely follow up on cardiac patients’ functional exercise capacity.
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