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Updated: Aug 7, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Using Item Response Times in Online Questionnaires to Detect Mild Cognitive Impairment
Stefan Schneider1,2, Doertes U Junghaenel1,2, Erik Meijer3
1Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, CA, USA.
Objectives:
With the increase in web-based data collection, response times (RTs) for survey items have become a readily available byproduct in most online studies. We examined whether RTs in online questionnaires can prospectively discriminate between cognitively normal respondents and those with cognitive impairment, no dementia (CIND).
Method:
Participants were 943 members of a nationally representative internet panel, aged 50 and older. We analyzed RTs that were passively recorded as paradata for 37 surveys (1,053 items) administered online over 6.5 years. A multilevel location-scale model derived 3 RT parameters for each survey: (1) a respondent's average RT and 2 components of intraindividual RT variability addressing (2) systematic RT adjustments and (3) unsystematic RT fluctuations. CIND status was determined at the end of the 6.5-year period.
Results:
All 3 RT parameters were significantly associated with CIND, with a combined predictive accuracy of area under the receiver-operating characteristic curve = 0.74. Slower average RTs, smaller systematic RT adjustments, and greater unsystematic RT fluctuations prospectively predicted a greater likelihood of CIND over periods of up to 6.5, 4.5, and 1.5 years, respectively.
Discussion:
RTs for survey items are a potential early indicator of CIND, which may enhance analyses of predictors, correlates, and consequences of cognitive impairment in online survey research.
