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      Psychometric Properties of NASA-TLX and Index of Cognitive Activity as Measures of Cognitive Workload in Older Adults

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          Abstract

          Cognitive workload is increasingly recognized as an important determinant of performance in cognitive tests and daily life activities. Cognitive workload is a measure of physical and mental effort allocation to a task, which can be determined through self-report or physiological measures. However, the reliability and validity of these measures have not been established in older adults with a wide range of cognitive ability. The aim of this study was to establish the test–retest reliability of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Index of Cognitive Activity (ICA), extracted from pupillary size. The convergent validity of these measures against event-related potentials (ERPs) was also investigated. A total of 38 individuals with scores on the Montreal Cognitive Assessment ranging between 17 and 30 completed a working memory test ( n-back) with three levels of difficulty at baseline and at a two-week follow-up. The intraclass correlation coefficients (ICC) values of the NASA-TLX ranged between 0.71 and 0.81, demonstrating good to excellent reliability. The mean ICA scores showed fair to good reliability, with ICCs ranging between 0.56 and 0.73. The mean ICA and NASA-TLX scores showed significant and moderate correlations (Pearson’s r ranging between 0.30 and 0.33) with the third positive peak of the ERP at the midline channels. We conclude that ICA and NASA-TLX are reliable measures of cognitive workload in older adults. Further research is needed in dissecting the subjective and objective constructs of cognitive workload.

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          Most cited references63

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          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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              STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

                Author and article information

                Journal
                Brain Sci
                Brain Sci
                brainsci
                Brain Sciences
                MDPI
                2076-3425
                16 December 2020
                December 2020
                : 10
                : 12
                : 994
                Affiliations
                [1 ]Laboratory for Advanced Rehabilitation Research in Simulation, Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, KS 66160, USA; pahmadnezhad@ 123456kumc.edu
                [2 ]Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA; kgustafson@ 123456kumc.edu (K.G.); wbrooks@ 123456kumc.edu (W.M.B.); jburns2@ 123456kumc.edu (J.M.B.)
                [3 ]Hoglund Brain Imaging Center, University of Kansas Medical Center, Kansas City, KS 66160, USA; kliao@ 123456kumc.edu
                [4 ]University of Kansas Alzheimer’s Disease Center, University of Kansas Medical Center, Kansas City, KS 66160, USA; jmahnken@ 123456kumc.edu
                [5 ]Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA
                Author notes
                [* ]Correspondence: hdevos@ 123456kumc.edu
                Author information
                https://orcid.org/0000-0002-8853-6840
                https://orcid.org/0000-0003-3225-5086
                https://orcid.org/0000-0001-6227-7636
                Article
                brainsci-10-00994
                10.3390/brainsci10120994
                7766152
                33339224
                eb6fabd2-0873-440f-888f-85979ff818b8
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 November 2020
                : 14 December 2020
                Categories
                Article

                event-related potentials,workload,reliability,working memory,mild cognitive impairment,dementia

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