Empirically derived user attributes for the design of home healthcare technologies. Issue 8 (December 2015)

• Record Type: Journal Article
• Authors: Burrows, Alison; Gooberman-Hill, Rachael; Coyle, David
• Is Part Of: Personal and ubiquitous computing. Volume 19:Issue 8(2015)
• Page Start: 1233
• Page End: 1245
• Publication Date: 2015-12
• Subjects: Smart home -- Healthcare -- User-centred design -- Ethnography -- User attributes -- Personas; Mobile computing -- Periodicals; Portable computers -- Periodicals; Human-computer interaction -- Periodicals; 004.16
• DOI: 10.1007/s00779-015-0889-1
• Languages: English
• ISSNs: 1617-4909

Abstract

Abstract Designing effective home healthcare technologies is a complex task. In order to succeed, it is important to look beyond purely technology-driven solutions and to develop technologies and services that are flexible and reflect a sensitive understanding of the diverse users of such systems. The key contribution of this paper is to introduce 15 empirically derived attributes that can help designers to build a more detailed understanding of the potential users of home healthcare systems. The attributes are spread across four broad themes: technology in the home, experiences of technology, experiences of health and care, and thoughts about smart home technology for health and care. These themes and attributes emerged from an ethnographic study in which we interviewed people across 15 households. All interviews took place in people's homes and were supplemented by home technology tours and cultural probes. It is intended that the 15 attributes be used in conjunction with demographic and household data to build a richer picture of personal experiences of home, health, and technology in real-life contexts. The aim was to provide an inclusive framework, based on empirically derived attributes, that helps to inform an overall user-centred design approach. To demonstrate one application of the attributes in design, the paper provides in-depth example of their use in the development of a rich set of data-driven personas.