Due to the demographical development, age-related diseases will drastically increase over the
next decades. To face this healthcare challenge, early and accurate identification of
cognitive impairment is crucial. The assessment of neurocognitive functioning ideally
requires a tool that is short, easy to administer and interpret, and has high diagnostic
accuracy. In this context, the use of computerized test batteries is receiving increasing
attention. Compared to paper-pencil tests, computerized test batteries have many advantages.
The possibility to measure reaction times may provide additional information. Moreover, test
questions are always presented the exact same way, examiner-related bias is eliminated, and
results are available immediately after examination. Due to the ability to adjust the level
of difficulty to the performance of the individual, floor and ceiling effects may be
minimized. Additionally, costs are reduced, and fewer materials and less trained personnel
are required. Finally, big data approaches and the use of machine learning algorithms are
becoming more popular in the field of clinical diagnostics, and computerized cognitive test
batteries may facilitate future data collection to this aim.
In 2014, we developed a self-administered tablet computer program for the iPad (CogCheck) to
assess preoperative cognitive functioning in surgery patients.
The cognitive tests used in the CogCheck application are identical or similar to the
paper-and-pencil tests that are currently used in dementia diagnostics. Replacing some of the
paper-and-pencil tests by a computerized test battery may facilitate the routine
neuropsychological examinations. Thus, we aim to investigate the diagnostic accuracy and
user-friendliness of CogCheck when applied in a cognitively impaired patient sample. In a
first step, the diagnostic properties of CogCheck will be examined by differentiating between
healthy controls and patients with mild or major neurocognitive disorder (NCD) predominantly
due to Alzheimer's disease (AD). Data from healthy controls have been collected (EKNZ
Req-2016-00393) in a previous normative study of CogCheck. Thus a further aim is to
investigate the user-friendliness of CogCheck in patients with mild or major NCD
predominantly due to AD.
The primary aim of our study is to investigate the diagnostic accuracy of CogCheck for
patients with mild or major NCD predominantly due to AD in a German-speaking population.
Secondary aims are: (1) to examine the user-friendliness of CogCheck in patients with mild or
major NCD predominantly due to AD, (2) to compare the results between cognitively healthy
individuals (EKNZ Req-2016-00393) and patients with mild or major NCD predominantly due to AD
on each of the CogCheck subtest, (3) to establish an algorithm with the CogCheck subtests
that optimally distinguishes between cognitively healthy controls (EKNZ Req-2016-00393) and
patients with mild or major NCD predominantly due to AD, (4) to compare the diagnostic
properties of CogCheck with the ones of the currently used paper-pencil tests.