Key factors in the engineering process for systems for aging in place contributing to low usability and success

Abstract

Purpose -Digital systems for independent aging, support and care are not being adopted as hoped. The purpose of this paper is to examine the results of three studies to derive key factors during the development and engineering process of care and support systems for older people that can impact acceptance and uptake to provide support to future projects. Design/methodology/approach -The paper analyzed the results of three qualitative studies, including two detailed case studies and a further study with 35 participants, to derive key factors. Methods for deriving factors are based on thematic analysis to identify common factors across cases and participants. Findings -The findings point to a broad set of interconnected factors that give developers of these types of systems specific recommendations. These highlight what makes these projects complex and identify implications for the development process. Furthermore, they show way the needed user-centered and iterative methods may be in conflict with funding processes. Originality/value -While others have reported on single projects or looked at acceptance, these studies were the first to explore aspects of the development process that may contribute to the lack of success to date of these types of systems. The results here support more successful outcomes in the future, both by helping people involved in the development of these systems to avoid some of the issues others face and providing input to improve the performance of the engineering process.

Usability: Adoption, Measurement, Value

“…Satisfaction was found to be the most prevalent measure across the literature (Akatsu & Miki, 2004;Garcia-Sanjuan et al, 2017;Hernández-Encuentra et al, 2009;Hsieh et al, 2018;McCarthy et al, 2007;Nilsson et al, 2003;Otjacques et al, 2009;Sonderegger et al, 2016;Wang et al, 2011;Ziefle & Bay, 2005). Less prevalent measures consisted of usefulness (Haslwanter et al, 2018), learnability (Garcia-Sanjuan et al, 2017;Petrovčič et al, 2018), affect state, and workload (Sonderegger et al, 2016).…”

The Limitations of User-and Human-Centered Design in an eHealth Context and How to Move Beyond Them

“…In addition, this belief in HCD as the best approach has influenced the writers of calls in funding programs (such as the European Union’s Horizon program), in which HCD is often included as a prerequisite for funding. Focusing calls in such a way pushes researchers to use an approach that may not be optimal for their context [ 24 ]. In all, it is crucial that the eHealth design community acknowledges the inherent limitations of HCD in eHealth, to (1) reduce the positive bias in their reflections on the application of HCD, (2) raise awareness among design teams about the limitations of this design approach, and (3) improve HCD processes for eHealth by accounting for the limitations of the approach.…”

An opinion mining methodology to analyse games for health

“…For a digital solution to be successful, it has to fit the specific needs of its users at the time of its construction and evolve to fit their changing needs later, which requires further refinements and iteration, for instance, when the solution is used in realistic conditions [ 13 ]. It is then important to identify ways in which user feedback could be continuously monitored beyond product launch.…”