Call for Papers
Focus Theme: “Machine Learning and Data Analytics in Pervasive Health”
Guest editors: Nuria Oliver, Oscar Mayora, Michael Marschollek
Deadline: July 28, 2017
Phone: +49 (0)711 - 2 29 87 88
Fax: +49 (0)711 - 2 29 87 65
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S. Wagner (1), T. S. Toftegaard (1), O. W. Bertelsen (2)
(1) Department of Engineering, Aarhus University, Aarhus, Denmark; (2) Department of Computer Science, Aarhus University, Aarhus, Denmark
Telemedicine, eHealth, self-care, medication adherence, Patient adherence
Background: Patients performing self-care in the unsupervised setting do not always adhere to the instructions they were initially provided with. As a consequence, a patient’s ability to successfully comply with the treatment plan cannot be verified by the treating healthcare professional, possibly resulting in reduced data quality and suboptimal treatment.
Objectives: The aim of this paper is to introduce the Adherence Strategy Engineering Framework (ASEF) as a method for developing novel technology-based adherence strategies to assess and improve patient adherence levels in the unsupervised setting.
Methods: Key concepts related to self-care and adherence were defined, discussed, and implemented as part of the ASEF framework. ASEF was applied to seven self-care case studies, and the perceived usefulness and feasibility of ASEF was evaluated in a questionnaire study by the case study participants. Finally, we reviewed the individual case studies usage of ASEF.
Results: A range of central self-care concepts were defined and the ASEF methodological framework was introduced. ASEF was successfully used in seven case studies with a total of 25 participants. Of these, 16 provided answers in the questionnaire study reporting ASEF as useful and feasible. Case study reviews illustrated the potential of using context-aware technologies to support self-care in the unsupervised setting as well as ASEF’s ability to support this.
Conclusion: Challenges associated with moving healthcare to the unsupervised setting can be overcome by applying novel context-aware technology using the ASEF method. This could lead to better treatment outcomes and reduce healthcare expenditures.
S. Wagner (1), C. H. Kamper (2), N. H. Rasmussen (1), P. Ahrendt (1), T. S. Toftegaard (1), O. W. Bertelsen (3)
Methods Inf Med 2014 53 3: 225-234
D. Gammon 1, 2, L. K. Johannessen 1, T. Sørensen 1, R. Wynn 3, P. Whitten4
Methods Inf Med 2008 47 3: 260-269
J. Gonzales (1) , S. Pomel (1), V. Breton (1), B. Clot (2), J. L. Gutknecht (3), B. Irthum(4) , Y. Legré (1,5)
Methods Inf Med 2005 44 2: 186-189