Abstract
To be more successful in preventing malnutrition for older adults living at home, there is a need for better methods to characterize their food behavior, as well as there is a need for health-supporting technologies focusing more on individualized contextual preferences. This study reveals how photos can be used to characterize older adults’ food-related behavior and preferences, and how photo elicitation can be used to design an eating environment in mixed reality for older solitary adults. This study is based on a sample of 22 older adults, who took in total 153 pictures of their meals, and a workshop using photo elicitation with 16 older adults in a community center. The findings revealed how photos can be used as a self-monitoring process to create meaningful and rich in-depth information on food-related behavior of older adults living at home. Photo elicitation can be used as a supplement to characterize older adults’ food-related behavior and preferences in a mixed reality environment. Further, we outline both advantages and limitations of using photo elicitation in a context of human-computer interaction.
| Originalsprog | Engelsk |
|---|---|
| Titel | HCI International 2022 – Late Breaking Papers : HCI for Health, Well-being, Universal Access and Healthy Aging |
| Redaktører | Vincent G. Duffy, Qin Gao, Jia Zhou, Margherita Antona, Constantine Stephanidis |
| Antal sider | 14 |
| Forlag | Springer |
| Publikationsdato | 2022 |
| Sider | 510-523 |
| ISBN (Trykt) | 978-3-031-17901-3 |
| ISBN (Elektronisk) | 978-3-031-17902-0 |
| DOI | |
| Status | Udgivet - 2022 |
| Begivenhed | International Conference on Human-Computer Interaction - Online Varighed: 26 jun. 2022 → 1 jul. 2022 Konferencens nummer: 24 https://2022.hci.international/ |
Konference
| Konference | International Conference on Human-Computer Interaction |
|---|---|
| Nummer | 24 |
| Lokation | Online |
| Periode | 26/06/2022 → 01/07/2022 |
| Internetadresse |
| Navn | Lecture Notes In Computer Science |
|---|---|
| Vol/bind | 13521 |
| ISSN | 0302-9743 |