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Updated: May 11, 2026
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Unobtrusive measurement of daily computer use to detect mild cognitive impairment
Jeffrey Kaye1, Nora Mattek2, Hiroko H Dodge2
1Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, OR, USA; Department of Neurology, Oregon Health & Science University, Portland, OR, USA; Neurology Service, Portland Veteran Affairs Medical Center, Portland, OR, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
Background:
Mild disturbances of higher order activities of daily living are present in people diagnosed with mild cognitive impairment (MCI). These deficits may be difficult to detect among those still living independently. Unobtrusive continuous assessment of a complex activity such as home computer use may detect mild functional changes and identify MCI. We sought to determine whether long-term changes in remotely monitored computer use differ in persons with MCI in comparison with cognitively intact volunteers.
Methods:
Participants enrolled in a longitudinal cohort study of unobtrusive in-home technologies to detect cognitive and motor decline in independently living seniors were assessed for computer use (number of days with use, mean daily use, and coefficient of variation of use) measured by remotely monitoring computer session start and end times.
Results:
More than 230,000 computer sessions from 113 computer users (mean age, 85 years; 38 with MCI) were acquired during a mean of 36 months. In mixed-effects models, there was no difference in computer use at baseline between MCI and intact participants controlling for age, sex, education, race, and computer experience. However, over time, between MCI and intact participants, there was a significant decrease in number of days with use (P = .01), mean daily use (∼1% greater decrease/month; P = .009), and an increase in day-to-day use variability (P = .002).
Conclusions:
Computer use change can be monitored unobtrusively and indicates individuals with MCI. With 79% of those 55 to 64 years old now online, this may be an ecologically valid and efficient approach to track subtle, clinically meaningful change with aging.
