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Updated: Jan 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Overnight sleep affects the stability of neuropsychological classification in mild cognitive impairment
Jun Ha Chang1, Gavin J Latona1, Matthew Rizzo1
1Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Introduction:
Reversion to normal cognition in mild cognitive impairment (MCI) is relatively common. This study tested whether total sleep time (TST) and sleep efficiency (SE) on the night before cognitive testing predicts MCI reversion.
Methods:
Fifty-eight community-dwelling older adults with MCI (mean age = 75 years) participated. MCI was defined as cognitive performance ≥ 1.5 standard deviation below norms in at least one domain, with preserved daily functioning. Participants completed cognitive testing at baseline and 1-year follow-up, and sleep was objectively measured using actigraphy at baseline. Logistic regression assessed whether prior-night TST and SE predicts reversion, adjusting for age and sex.
Results:
Sixteen participants (27.6%) reverted. Shorter prior-night TST was associated with higher odds of reversion, whereas SE showed no significant association. Random night or chronic sleep measures showed no associations.
Discussion:
Acute sleep disruption may transiently impair cognition and contribute to MCI misclassification. Sleep screening before testing may improve diagnostic reliability.
Highlights:
Shorter total sleep time on the night before cognitive testing was associated with higher odds of mild cognitive impairment (MCI) reversion after 1 year. Sleep efficiency, random-night sleep, and 14-day average sleep showed no significant association with MCI reversion. Single-domain MCI cases were more likely to revert than multi-domain MCI cases, suggesting greater susceptibility to acute factors such as poor sleep. Brief screening of prior-night sleep or actigraphy-derived metrics could help identify low-reliability assessment and improve diagnostic stability in MCI research.
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