Remote self-administration of digital cognitive tests using the Brief Assessment of Cognition: Feasibility, reliability, and sensitivity to subjective cognitive decline

Abstract

Cognitive impairment is a common and pervasive feature of etiologically diverse disorders of the central nervous system, and a target indication for a growing number of symptomatic and disease modifying drugs. Remotely acquired digital endpoints have been recognized for their potential in providing frequent, real-time monitoring of cognition, but their ultimate value will be determined by the reliability and sensitivity of measurement in the populations of interest. To this end, we describe initial validation of remote self-administration of cognitive tests within a regulatorily compliant tablet-based platform. Participants were 61 older adults (age 55+), including 20 individuals with subjective cognitive decline (SCD). To allow comparison between remote (in-home) and site-based testing, participants completed 2 testing sessions 1 week apart. Results for three of four cognitive domains assessed demonstrated equivalence between remote and site-based tests, with high cross-modality ICCs (absolute agreement) for Symbol Coding (ICC = 0.75), Visuospatial Working Memory (ICC = 0.70) and Verbal Fluency (ICC > 0.73). Group differences in these domains were significant and reflected sensitivity to objective cognitive impairment in the SCD group for both remote and site-based testing (p < 0.05). In contrast, performance on tests of verbal episodic memory suggested inflated performance during unmonitored testing and indicate reliable use of remote cognitive assessments may depend on the construct, as well as the population being tested.

Comparing a Computerized Digit Symbol Test to a Pen-and-Paper Classic

“…Notably, this relationship is not as strong as what has been observed with the Brief Assessment of Cognition (BAC) app and remote version of the same measure, which has a correlation of about 0.8 with the BSC in 3 different populations (not tested in CHR populations). 36 , 37 In the BAC app version, participants enter numbers on a digital keypad below the prompted symbol. It may be that the symbols each have a unique number associated with them, like the BSC, which might foster a stronger association between the symbols and the numbers.…”

“…Additionally, the Brief Assessment of Cognition app has a version that is highly correlated with the paper version, but it requires special equipment and software to administer. 36 , 37 While there has been tremendous utility in these computerized processing speed tasks, they have not yet been validated as an easily scalable replacement for the classic pen-and-paper version in the CHR population.…”

Clinical and Cognitive Phenotyping of Copy Number Variants Pathogenic for Neurodevelopmental Disorders From a Multi-Ancestry Biobank

“…The reliability and validity of each cognitive test as well as evidence to support remote administration has been previously demonstrated for use in schizophrenia spectrum disorders and in other populations (Supplementary Methods). [27][28][29][30][31][32][33][34] Statistical Analyses: Cognitive raw scores were used as outcomes for linear regression analyses and group comparisons, with age, sex and ancestry included as covariates. All analyses were performed using R, version 4.0.4.…”

Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease

“…Common digital measures focus on physical activity (e.g., number or steps from accelerometers), and vital signs (e.g., heart rate from photoplethysmography). Successful applications of digital measures also include cognition ( 25 , 26 ), gait and mobility ( 27 ), pain ( 28 , 29 ) and stress ( 29 ). A feasibility study was previously conducted on healthy participants to explore the relationship between self-reported, non-pathological physical and mental fatigue and behavioural and physiological time-series data acquired from a multisensor wearable device ( 30 ).…”