Welcome to the SAMS study
SAMS is a three-year project that is investigating novel data and text mining techniques for detecting subtle signs of Cognitive Dysfunction, in elderly computer users, that may indicate the early stages of dementia.
If we can detect early signs of dementia, even before people are aware of problems, then early referral for expert diagnosis and treatment can slow progress of the disease and in the longer term enhance possible treatments.
SAMS (Software Architecture for Mental health Self management) is a three-year project that will investigate whether monitoring data from computer-use activity can be used to effectively detect subtle signs of cognitive impairment that may indicate the early stages of Alzheimer's disease.
Promoting self-awareness of change in cognitive function is a key step in encouraging people to self-refer for clinical evaluation. A key motivation for SAMS, therefore, is to provide a non-invasive tool that helps develop such self-awareness.
An increasing number of older people, the group most at risk of cognitive dysfunction and dementia, are regularly using the Internet for social communication, keeping in touch with families, particularly grandchildren using e-mail and social networking sites. This Internet activity presents an opportunity to harness the rich routinely available information about the changes in linguistic, executive and motor speed abilities that people experience over time.
Work is needed to develop the software to harness this opportunity, to establish the optimal thresholds for flagging up important changes in cognition and the optimal methods for conveying this information back to individuals. SAMS will validate thresholds by examining changes in performance in people with mild cognitive impairment and mild Alzheimer's disease and begin to explore the potential for technology-enhanced detection of early cognitive dysfunction. Patterns of computer use and content of e-mails will be analysed and coupled to feedback mechanisms to enhance users' cognitive self-awareness, empowering them to self-administer follow up tests and decide when to self-refer for expert medical advice.
Tackling cognitive change detection using computer use data requires the pooling of knowledge and integration of techniques from various disciplines including cognitive neuroscience, clinical psychology and computer science. In addition to developing techniques for MCI detection and supporting self- referral, an explicit goal of the research is to develop a generic sense making and user-centred feedback architecture. This could be applied to a wide range of problems where interpreting computer use may be appropriate, e.g. mental health, social loneliness, privacy and social exploitation.