The ecological validity of the Uniform Data Set 3.0 neuropsychological battery in individuals with mild cognitive impairment and dementia

Abstract

Objective: Ecological validity refers to the ability of neuropsychological measures to predict real world performance. Questions remain as to the ecological validity of commonly used measures, particularly regarding their relationships to global versus specific activities of daily living among those with neurodegenerative disease. We explored these issues through the lens of the Uniform Data Set 3.0 Neuropsychological battery (UDS3NB) in individuals with mild cognitive impairment and dementia. Method: UDS3NB and informant rated Functional Activities Questionnaire scales were evaluated from 2,253 individuals with mild cognitive impairment and dementia. Ordinal regression equations were used to explore the relationships of demographic and cognitive variables with overall and specific instrumental activities of daily living. Results: Delayed recall for visual and verbal material, and performance on trail making tests were consistent predictors of global and specific functions. Specific skills (i.e. naming or figure copy) showed differential relationships with specific activities, while phonemic fluency was not related to any particular activity. Conclusions: Measures in the UDS3NB predicted activities of daily living in individuals with MCI and dementia, providing initial support for the ecological validity of these tests. Specifically, measures that tap core deficits of Alzheimer’s disease, such as delayed recall and sequencing/shifting, are consistent predictors of performance in daily tasks.

Keywords:

• Ecological validity
• Alzheimer’s disease
• dementia
• neuropsychological testing
• mild cognitive impairment
• activities of daily living

Disclosure statement

The authors have no disclosures relevant to this publication

Additional information

Funding

This work was supported by an Alzheimer’s Association Research Fellowship (2019-AARF-641693 PI Andrew Kiselica, PhD) and the 2019-2020 National Academy of Neuropsychology Clinical Research Grant (PI Andrew Kiselica, PhD). The NACC database is funded by National Institute on Aging/National Institutes of Health (U01 AG016976). NACC data are contributed by the National Institute on Aging-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI: Neil Kowall, MD), P50 AG008702 (PI: Scott Small, MD), P50 AG025688 (PI: Allan Levey, MD, PhD), P50 AG047266 (PI: Todd Golde, MD, PhD), P30 AG010133 (PI: Andrew Saykin, PsyD), P50 AG005146 (PI: Marilyn Albert, PhD), P50 AG005134 (PI: Bradley Hyman, MD, PhD), P50 AG016574 (PI: Ronald Petersen, MD, PhD), P50 AG005138 (PI: Mary Sano, PhD), P30 AG008051 (PI: Thomas Wisniewski, MD), P30 AG013854 (PI: M. Marsel Mesulam, MD), P30 AG008017 (PI: Jeffrey Kaye, MD), P30 AG010161 (PI: David Bennett, MD), P50 AG047366 (PI: Victor Henderson, MD, MS), P30 AG010129 (PI: Charles DeCarli, MD), P50 AG016573 (PI: Frank LaFerla, PhD), P50 AG005131 (PI: James Brewer, MD, PhD), P50 AG023501 (PI: Bruce Miller, MD), P30 AG035982 (PI: Russell Swerdlow, MD), P30 AG028383 (PI: Linda Van Eldik, PhD), P30 AG053760 (PI: Henry Paulson, MD, PhD), P30 AG010124 (PI: John Trojanowski, MD, PhD), P50 AG005133 (PI: Oscar Lopez, MD), P50 AG005142 (PI: Helena Chui, MD), P30 AG012300 (PI: Roger Rosenberg, MD), P30 AG049638 (PI: Suzanne Craft, PhD), P50 AG005136 (PI: Thomas Grabowski, MD), P50 AG033514 (PI: Sanjay Asthana, MD, FRCP), P50 AG005681 (PI: John Morris, MD), and P50 AG047270 (PI: Stephen Strittmatter, MD, PhD).