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Background: Existing cognitive screening measures fall short in capturing preclinical AD, and cognitive impairment is unrecognized or misdiagnosed in 27‐81% of older adult primary care patients. Digital assessment technology has the potential to deliver more efficient and sensitive cognitive screening, but requires rigorous validation first. We aimed to determine the accuracy of remote (M2C2 mobile app) and clinic‐based (TabCAT and DCTclockTM) digital tests to distinguish between older adults with and without AD pathologic change. We used the Montreal Cognitive Assessment (MoCA) as a reference standard comparison test. Methods: We recruited 73 cognitively normal participants with Aß PET status (Aß+, n = 25; Aß‐, n = 48, determined by clinical read) from the Butler Hospital Alzheimer's Prevention Registry (mean age = 69.2 and education = 16.5; 71% female; 89% White). Participants completed M2C2 tasks at home 3 times per day for 8 days, followed by the TabCAT tasks, DCTclockTM, and MoCA at an in‐person study visit. We calculated the area under the curve (AUC) to compare cognitive task accuracy to distinguish Aß status. Multi‐day learning curves were used to examine differences in M2C2 task performance by Aß status over time. Results: Among the M2C2 tasks, average performance on the Prices task (episodic memory) over 8 days showed the highest accuracy (AUC =.77) to distinguish Aß status. The Aß+ group tended to perform worse than the Aß‐ group on the Prices task over time (Figure 1). On in‐person screening measures (single time‐point), accuracy to distinguish Aß was greatest for the TabCAT Favorites task (AUC =.76), relative to the DCTclockTM (AUC =.72) and the MoCA (AUC =.71). Conclusions: We showed that several brief digital screening approaches (memory‐specific tasks on the M2C2 and TabCAT) outperform the MoCA in distinguishing between cognitively healthy individuals with and without elevated cerebral Aβ. Although further validation in community and clinic‐based samples is needed, these results suggest that these digital cognitive assessments may be suitable for more widespread screening to detect early pathological changes in neurodegenerative disorders. Figure 1. M2C2 task performance over time (3 sessions daily for 8 days) by Aβ status. [ABSTRACT FROM AUTHOR]

Background: Existing cognitive screening measures fall short in capturing preclinical AD, and cognitive impairment is unrecognized or misdiagnosed in 27‐81% of older adult primary care patients. Digital assessment technology has the potential to deliver more efficient and sensitive cognitive screening, but requires rigorous validation first. We aimed to determine the accuracy of remote (M2C2 mobile app) and clinic‐based (TabCAT and DCTclockTM) digital tests to distinguish between older adults with and without AD pathologic change. We used the Montreal Cognitive Assessment (MoCA) as a reference standard comparison test. Method: We recruited 73 cognitively normal participants with Aß PET status (Aß+, n = 25; Aß‐, n = 48, determined by clinical read) from the Butler Hospital Alzheimer's Prevention Registry (mean age = 69.2 and education = 16.5; 71% female; 89% White). Participants completed M2C2 tasks at home 3 times per day for 8 days, followed by the TabCAT tasks, DCTclockTM, and MoCA at an in‐person study visit. We calculated the area under the curve (AUC) to compare cognitive task accuracy to distinguish Aß status. Multi‐day learning curves were used to examine differences in M2C2 task performance by Aß status over time. Result: Among the M2C2 tasks, average performance on the Prices task (episodic memory) over 8 days showed the highest accuracy (AUC =.77) to distinguish Aß status. The Aß+ group tended to perform worse than the Aß‐ group on the Prices task over time (Figure 1). On in‐person screening measures (single time‐point), accuracy to distinguish Aß was greatest for the TabCAT Favorites task (AUC =.76), relative to the DCTclockTM (AUC =.72) and the MoCA (AUC =.71). Conclusion: We showed that several brief digital screening approaches (memory‐specific tasks on the M2C2 and TabCAT) outperform the MoCA in distinguishing between cognitively healthy individuals with and without elevated cerebral Aβ. Although further validation in community and clinic‐based samples is needed, these results suggest that these digital cognitive assessments may be suitable for more widespread screening to detect early pathological changes in neurodegenerative disorders. Figure 1. M2C2 task performance over time (3 sessions daily for 8 days) by Aβ status. [ABSTRACT FROM AUTHOR]

Background: Previous research shows 1) clock drawing performance slows for older adults after total knee arthroplasty (TKA; Hizel et al., 2018), and 2) brain‐behavioral associations with intra‐individual variability on an EEG‐derived index during TKA (Hernaiz et al., 2019). The current study expanded upon these findings by examining how preoperative cognition and intraoperative responses to anesthesia may predict postoperative clock drawing performance. We hypothesized greater intraoperative intra‐individual BIS variability would explain a significant portion of variance in four primary latency measures to command and copy condition at 3‐weeks following surgery. Method: The final prospectively recruited IRB consented participant sample included 54 non‐demented patients age 60+ receiving TKA under general anesthesia. Consistent with Hernaiz et al., (2019), derived 2‐channel EEG variability was studied from tourniquet inflation to tourniquet release, thereby establishing a consistent period of examination. Intra‐individual variability was calculated as the squared deviation from the mean BIS value over the duration of measurement. Clock drawing to command and copy was recorded with digital pen technology and scored with semiautomatic rater‐guided software (Souillard‐Mandar et al., 2016). Outcome latency variables of interest: 1) total completion time in seconds (TCT), 2) time prior to setting the first hand in seconds (pre‐first hand latency; PFHL), 3) seconds after drawing the clock face to next pen stroke (post‐clock face latency; PCFL). Result: At 3‐weeks, participants took, on average, six seconds longer to complete the drawing compared to baseline performance, and two seconds longer to set the first clock hand. Correcting for baseline performance, separate hierarchical regressions showed intra‐individual EEG‐derived variability explained an additional 4.3% of variance in postoperative command TCT (F(2,51)=28.301, p<.038, R2=.536, ∆R2=.043), such that those with greater intraoperative BIS variability took longer to complete the drawing to command. Conclusion: In non‐demented older adults undergoing TKA, higher intra‐individual BIS variability significantly contributed to total clock drawing time during the command condition. Command total completion time is sensitive to numerous cognitive domains including processing speed, language, working memory, and declarative memory (Dion et al., 2020). Fluctuations during general anesthesia as recorded on an EEG‐derived index may help explain postoperative slowing during clock drawing at 3‐weeks post‐surgery. [ABSTRACT FROM AUTHOR]