Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment.

ABSTRACT:

Background

Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.

Methods

We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnose

SUBMITTER: Rykov YG 

PROVIDER: S-EPMC10809621 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature