Blog
Assessments of ADAS-Cog administration quality are comparable across expert and non-expert reviewers
This poster was presented at the ISCTM Annual Scientific Meeting in February 2023.
Age-Related Cognitive Changes in the CANTAB Normative Sample
This poster was presented at the ISCTM Annual Scientific Meeting in February 2023.
Interim Analysis of Online Screening as a Recruitment Strategy for MOPEAD
At AAIC 2019 we presented an interim analysis of online screening as a recruitment strategy for the MOPEAD initiative. The initial findings are positive, with high levels of engagement from older adults, as well as sensitivity to impairment.
Large-Scale Remote Assessment of Verbal Cognitive Function Using Automatic Speech Recognition
At AAIC 2019, we shared how to administer and score verbal neuropsychological assessments on participants own devices in a way that is acceptable, feasible and reliable.
Vocalic Markers of Cognitive Load Derived from Automated Verbal Neuropsychological Assessment
The effort participants exert in order to complete a task provides the researcher with a rich understanding of their level of functioning. At AAIC 2019, we shared how to derive this cognitive load from voice recordings.
Verbal paired associates: Effects of age and education in automated remote testing
New data presented at AD/PD 2019 demonstrated the feasibility and validity of conducting automated, verbal recall tasks on participants’ own devices. This research has implications for recruitment and monitoring in Alzheimer’s disease trials.
Domain-specific cognitive impairment in Parkinson’s disease detected using touchscreen technology
At AD/PD 2019 we showed that automated, electronic cognitive testing is acceptable and feasible for patients presenting to clinic with Parkinson’s disease.
Inferring cognitive load from voice: Enriching automated cognitive testing with vocalics
Traditional cognitive measures struggle to demonstrate when a participant is compensating for their deficits. At AD/PD 2019 we shared new data on how to overcome this issue: deriving cognitive load from voice data collected on participants’ own devices, and in their own homes.
