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The survey for memory, attention, and reaction time (SMART): Preliminary normative online panel data and user attitudes for a brief web-based cognitive performance measure.

Mira Isabelle LeeseNora MattekJohn P K BernsteinKatherine E DorociakSarah GothardJeffrey A KayeAdriana M Hughes
Published in: The Clinical neuropsychologist (2022)
Objective The Survey for Memory, Attention, and Reaction Time (SMART) was recently introduced as a brief (<5 min), self-administered, web-based measure of cognitive performance in older adults. The purpose of this study was threefold: (1) to develop preliminary norms on the SMART; (2) to examine the relationship between demographic variables (i.e. age, sex, education), device type used, and SMART performance; and (3) to assess user attitudes of the SMART. Method A sample of 1,050 community-dwelling adults ( M age =59.5 (15.2), M education = 16.5 (2.1), 67.1% female, 96% white) was recruited from an ongoing web-based research cohort. Participants completed the SMART, which consists of four face-valid cognitive tasks assessing visual memory, attention/processing speed, and executive functioning. SMART performance outcome metrics were subtest completion time (CT), click count, and total CT. Participants provided demographic information and completed a survey of user attitudes toward the SMART (i.e. usability, acceptability). Results Older age was the only demographic variable associated with slower SMART total CT ( r = .60, p <.001). Education was not associated with SMART CT or click counts overall ( p > .05). Male sex was generally associated with longer SMART CT ( p < .001, partial eta squared = .14) on all sub-tests. Regarding acceptability, 97.3% indicated willingness to take the SMART again, with more than half willing to complete it on a weekly basis. Conclusion The preliminary normative data on the SMART indicates that it is a feasible and well-accepted web-based cognitive assessment tool that can be administered on multiple device platforms.
Keyphrases
  • working memory
  • computed tomography
  • healthcare
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  • physical activity
  • mental health
  • positron emission tomography
  • magnetic resonance
  • big data
  • machine learning
  • middle aged