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Current conventional cognitive assessments are limited in their efficiency and sensitivity,
often relying on a single score such as the total items correct. Typically, multiple
features of response go uncaptured. The use of digital technologies allows for these
response characteristics to be readily captured and automatically analyzed thus increasing
the potential power of assessments. As a proof of this concept, we analyzed the Digit
Span (DS) task where conventionally, total number of correctly recalled digits in
both forward DS (FDS) and backward DS (BDS) tasks across test items constructs the
test score. This scoring method pays equal attention to all presented items and ignores
the count of incorrectly recalled digits. Additionally, the length of prompts adds
to the complexity of test performance. Our objective in this study is to explore a
new set of automatically derived biomarkers from DS test that address some of the
drawbacks in the conventional scoring and are also useful for distinguishing subjects
with MCI from those with intact cognition.
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