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The influence of culture on older adults' adoption of smart home monitoring| A qualitative descriptive studyFritz, Roschelle Lynnette 04 November 2015 (has links)
<p> This qualitative descriptive interdisciplinary study, performed in the space between nursing and engineering and within the field of gerontechnology, explored the influence of culture on older adults’ adoption of smart home monitoring. In-depth email interviews were used with a purposive sample of older adults (n=21) age 65 and older from Washington, Idaho, Alabama, Tennessee, and New York. Participants were asked to prospectively consider the question of adoption of a smart home that is under development at Washington State University’s Center for Advanced Studies in Adaptive Systems (CASAS) that combines artificial intelligence software with sensor monitoring for the purpose of maintaining safety and health. This smart home learns the resident’s motion patterns and can take an action on behalf of the resident living in the home. Many participants indicated a prospective openness to smart home monitoring. Openness depended on (a) the level and specificity of need and whether the smart home would meet that need, (b) perceived loss of privacy compensated by a feeling of safety and a receipt of health-assistance, (c) functionality, and (d) cost. Low inference themes emerging from rich text and supported with multiple lines of participants’ own words were <i> privacy, pride and dignity, family, trust, being watched, human touch, features and functionality, cost, and timing.</i> Participants were asked to self-identify their own culture of socially constructed values, which were found to heavily inform perceptions of privacy, independence, and family. Findings from this study explicate and illuminate older adults’ perceptions and descriptions of smart home monitoring, the relation to their own socially constructed values, and the influence on a decision to adopt or not adopt smart home monitoring. Findings may be used to inform future smart home design using machine learning, marketing, clinical nursing practice, nursing education, health policy, interdisciplinary collaboration, and research.</p>
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