ABSTRACT
This chapter explores the advantages and limitations of lifestyle monitoring (LSM) devices deployed in a telecare installation in an extra care housing setting for older people with impaired vision in south-west England, and discusses the attitudes and perceptions of the older people and their professional caregivers towards the technological devices that shared their homes and monitored their everyday lives. The uneasy relationship between people and technology is particularly exposed when it comes to issues relating to gerontechnology, that is, technology that is meant to support people in later life. Older people’s attitudes towards such ‘caring technologies’ have hitherto not been sufficiently mapped. This account, which is based on the results of an 11-month-long telecare trial, synthesises feedback gathered from four rounds of in-depth interviews with six older participants, gauging their understanding of the technology and tracing their changing attitudes towards the sensors installed in their homes. These are compared with the findings from three rounds of interviews with seven professional care staff from the housing scheme, designed to detect the impact of telecare on their working lives and caregiving practices. Older people’s attitudes towards various domestic objects bear heavily on their dispositions towards installed telecare devices, giving rise to a hierarchy of sensors that explains the varied degree of sensitivity and intrusiveness associated with sensoring different objects within the home environment. Moreover, as the case of the fall detector suggests, an immature and imperfect technology can even reverse the direction of the caring relationship, forcing older people to look after their troublesome devices. Although LSM proved capable of identifying changes in daily routines around the time of important health-related events, the retrospective interpretation of these changes required a large amount of contextual information and the involvement of the participants themselves. Furthermore, the findings to date highlighted technical and operational difficulties that need to be resolved before it will be possible to use LSM predictively.
