TY - JOUR
T1 - Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
AU - Butler, Paul Monroe
AU - Yang, Jenny
AU - Brown, Roland
AU - Hobbs, Matt
AU - Becker, Andrew
AU - Penalver-Andres, Joaquin
AU - Syz, Philippe
AU - Muller, Sofia
AU - Cosne, Gautier
AU - Juraver, Adrien
AU - Song, Han Hee
AU - Saha-Chaudhuri, Paramita
AU - Roggen, Daniel
AU - Scotland, Alf
AU - Silveira, Natalia
AU - Demircioglu, Gizem
AU - Gabelle, Audrey
AU - Hughes, Richard
AU - Erkkinen, Michael G.
AU - Langbaum, Jessica B.
AU - Lingler, Jennifer H.
AU - Price, Pamela
AU - Quiroz, Yakeel T.
AU - Sha, Sharon J.
AU - Sliwinski, Marty
AU - Porsteinsson, Anton P.
AU - Au, Rhoda
AU - Bianchi, Matt T.
AU - Lenyoun, Hanson
AU - Pham, Hung
AU - Patel, Mithun
AU - Belachew, Shibeshih
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/3
Y1 - 2025/3
N2 - Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
AB - Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
UR - https://www.scopus.com/pages/publications/86000360259
UR - https://www.scopus.com/inward/citedby.url?scp=86000360259&partnerID=8YFLogxK
U2 - 10.1038/s41591-024-03475-9
DO - 10.1038/s41591-024-03475-9
M3 - Article
C2 - 40038507
AN - SCOPUS:86000360259
SN - 1078-8956
VL - 31
SP - 829
EP - 839
JO - Nature Medicine
JF - Nature Medicine
IS - 3
M1 - 7690
ER -