Predictors of COgnitive DECline using digital devices - CODEC II

  • Research type

    Research Study

  • Full title

    Predictors of Cognitive Decline in Attenders of Essex Memory Clinic From Digital Devices Using Classical Statistical Methods, Machine Learning and Disease Progression Modelling

  • IRAS ID

    289028

  • Contact name

    Zuzana Walker

  • Contact email

    z.walker@ucl.ac.uk

  • Sponsor organisation

    University College London

  • Clinicaltrials.gov Identifier

    Z6364106/2021/08/27 , Data Protection Number

  • Duration of Study in the UK

    2 years, 11 months, 31 days

  • Research summary

    The overall aim of this study is to find out if people with cognitive difficulties will wear and use different types of digital technology, and if they will allow data from that technology and their clinical profile to be collected. Participants will be patients in Essex Memory clinic and their partners/carers. The digital technology used will include a smartwatch, a sleep headband and two smartphone applications, which have been selected as part of the Early Detection of Neurodegenerative Disease (EDoN) initiative. We will also investigate how we can analyse the digital data together with routinely captured clinical data using machine learning models, a complex type of statistical analysis.
    Further versions of the digital toolkit will be produced as feedback is collected from this pilot, and as new technologies are developed. This pilot study will therefore inform the subsequent roll-out of digital technologies and data collection in future cohorts that will collaborate with EDoN.
    The aim of the wider EDoN initiative is to combine digital and clinical data to develop machine learning (‘fingerprint’) models which can predict individuals’ risk of developing dementia decades before the onset of symptoms. The ultimate aim is to integrate the measurement of the most predictive low-burden clinical and digital markers into a cost-effective, non-invasive digital tool capable of delivering personalised disease detection at a population level, for instance as part of annual health checks.

  • REC name

    East Midlands - Leicester South Research Ethics Committee

  • REC reference

    22/EM/0062

  • Date of REC Opinion

    16 May 2022

  • REC opinion

    Further Information Favourable Opinion