SAMOA: Spatial Analytics through Mobile sensing of early AD
Research type
Research Study
Full title
SAMoA: Spatial Analytics through Mobile sensing of early Alzheimer’s disease
IRAS ID
215418
Contact name
Dennis Chan
Contact email
Sponsor organisation
Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge
Duration of Study in the UK
3 years, 0 months, 1 days
Research summary
The aim of this study is to investigate a novel approach to detecting Alzheimer’s disease (AD) in its very earliest stages, prior to the onset of dementia. There is an urgent need to identify new tests that are both diagnostically accurate but also usable on a large scale, given that millions of older people at risk of AD. Current tests do not meet this need. Traditional “pen-and-paper” tests of memory are not sensitive or specific for early AD, whereas sensitive tests, such as nuclear medicine scanning or spinal fluid testing, are expensive, invasive and available only in select university hospitals.
This project will address this need by examining people’s navigation via a smartphone app. Navigation is controlled by brain regions affected at the beginning of AD, and so it is hypothesised that altered navigational behaviour is an early sign of the disease. The ubiquitous use of smartphones offers a mean to embed diagnostics within everyday technologies.
Location tracking technology is already widely used (global positioning systems, Google Maps). We have developed smartphone apps that are capable of tracking movements frequently, and this allows us to build up maps of people’s locations, and their movement between locations. These can then be analysed in terms of number of locations visited, paths taken between locations, efficiency of paths.
The study will focus on two issues. First, given the wealth of information within people’s day to day movements, significant computer science-based modelling and data analysis will be needed to extract these measures of navigational behaviour. The second is that of preserving privacy. This will be achieved by removing all user-sensitive information from the data (put another way, we are interested in knowing how people get from A to B to C and back to A , but not in what A, B and C are).REC name
East of England - Cambridge Central Research Ethics Committee
REC reference
17/EE/0011
Date of REC Opinion
14 Jun 2017
REC opinion
Further Information Favourable Opinion