When we think of technology-savvy consumers, older adults are typically not the first persons that come to mind. The common misconception is that older adults do not want to use or cannot use technology. But for an increasing number of older adults, this is not true (Pew Internet and American Life Project 2003). Older adults do use technologies similar to their younger counterparts, but perhaps at different usage rates. Previous research has identified that there may be subgroups of older adults, "Silver Surfers", whose adoption patterns mimic younger adults (Pew Internet and American Life Project 2003). Much of the previous research on age-related differences in technology usage has only investigated usage broadly-from a "used" or "not used" standpoint. The present study investigated age-related differences in overall usage of technologies, as well as frequency of technology usage (i.e., never, occasional, or frequent). The data were gathered through a questionnaire from younger adults (N=430) and older adults (N=251) in three geographically separate and ethnically diverse areas of the United States. We found that younger adults use a greater breadth of technologies than older adults. However, age-related differences in usage and the frequency of use depend on the technology domain. This paper presents technology usage and frequency data to highlight age-related differences and similarities. The results provide insights into older and younger adults' technology-use patterns, which in turn provide a basis for expectations about knowledge differences. Designers and trainers can benefit from understanding experience and knowledge differences.

Older adults may encounter automated systems in a variety of context such as health care and transportation. Consequently it is important to understand the interactions between system knowledge and reliance. In this ongoing study, we tested 19 older adults on their ability to form an accurate mental model and how they responded to an automated navigation aid that was 70% and 100% reliable. Some older adults were able to form highly accurate mental models and were able to detect when the collaborative automated system was faulty. However, most of the older adults did not form accurate mental models and were likely to inappropriately trust the automation. Training to augment cognition may be helpful for older adults who use collaborative automated systems and have difficulty developing a highly accurate mental model.

Previous research has identified age-related differences in the use of computers and technology, with adoption mediated by cognitive variables and psychological factors [1]. Yet, the finding that younger adults used the Internet for significantly more activities than older adults suggests that other factors may be more important than ability differences alone. In this paper, we report preliminary findings on technology usage from data collected by the Center for Research and Education on Aging and Technology Enhancement (CREATE). We analyzed technology usage patterns across domains and types of technology to assess relationships between user characteristics and technology variables. We compared our findings with those from other research to identify potential implications for gerontechnology research and design.