Sleep estimation from wrist activity in patients with major depression

Abstract

Actigraphy has been used to monitor individuals' sleep and wakefulness patterns without laboratory confinement. To date, its validity in monitoring sleep and wakefulness among patients with major depressive episodes has not been systematically examined. The present study investigated whether the normative criteria of the Actigraph Data Analysis Software, initially optimized for healthy individuals, could score wrist-activity data accurately in a sample of depressed patients. Application of the normative algorithm yielded a correlation coefficient of 0.85 and an average error of 35 min, comparing actigraphic and polysomnographic sleep estimates. The algorithm optimized for this sample provided a correlation coefficient of 0.81 and an error of 6 minutes. For both algorithms, agreement for individual comparisons varied substantially. These findings suggest that scoring criteria optimized on wrist-activity data of healthy young adults may not produce optimal results for patients characterized with major depressive episodes.