Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
Butler, Paul Monroe; Yang, Jenny; Brown, Roland; Hobbs, Matt; Becker, Andrew; Penalver-Andres, Joaquin; Syz, Philippe; Muller, Sofia; Cosne, Gautier; Juraver, Adrien; Song, Han Hee; Saha-Chaudhuri, Paramita; Roggen, Daniel; Scotland, Alf; Silveira, Natalia; Demircioglu, Gizem; Gabelle, Audrey; Hughes, Richard; Erkkinen, Michael G.; Langbaum, Jessica B.; Lingler, Jennifer H.; Price, Pamela; Quiroz, Yakeel T.; Sha, Sharon J.; Sliwinski, Marty; Porsteinsson, Anton P.; Au, Rhoda; Bianchi, Matt T.; Lenyoun, Hanson; Pham, Hung; Patel, Mithun; Belachew, Shibeshih
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.
Link | PDF (Nature Medicine) [Open Access]
Butler, Paul Monroe; Yang, Jenny; Brown, Roland; Hobbs, Matt; Becker, Andrew; Penalver-Andres, Joaquin; Syz, Philippe; Muller, Sofia; Cosne, Gautier; Juraver, Adrien; Song, Han Hee; Saha-Chaudhuri, Paramita; Roggen, Daniel; Scotland, Alf; Silveira, Natalia; Demircioglu, Gizem; Gabelle, Audrey; Hughes, Richard; Erkkinen, Michael G.; Langbaum, Jessica B.; Lingler, Jennifer H.; Price, Pamela; Quiroz, Yakeel T.; Sha, Sharon J.; Sliwinski, Marty; Porsteinsson, Anton P.; Au, Rhoda; Bianchi, Matt T.; Lenyoun, Hanson; Pham, Hung; Patel, Mithun; Belachew, Shibeshih
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.
Link | PDF (Nature Medicine) [Open Access]
