A Multitrait, Multimethod Matrix Approach For A Consumer-Grade Wrist-Worn Watch Measuring Sleep Duration And Continuity

Study Objectives: We examined associations between self-reports about typical sleep patterns and sleep data derived from a wearable device worn on a nightly basis for a prolonged period (mean = 214 nights). We hypothesized that sleep characteristics would correlate better across different methods of assessment (self-report versus wearable) than they would correlate within the same method, a classic psychometric approach (multitrait, multimethod matrix).Methods: A cross-national sample of 6,230 adult wearable users completed a brief sleep questionnaire collecting data on sleep duration and number of awakenings (NAW) and provided informed consent to link their responses to data from their wearable watches. The data collection for the wearable occurred over 12 months and the sleep questionnaire was completed subsequent to that.Results: Results indicated a large (r = .615) correlation between sleep duration as assessed with the wearable and by self-report. A medium-to-large correlation (r = .406) was also seen for NAW. The multitrait, multimethod matrix suggested minimal method variance, i.e. similar "traits" (sleep duration and NAW) correlated across methods but within a given method, and such "traits" were generally unrelated.Conclusions: The results suggest that the longer period of data collection with the wearable generates more stable estimates of sleep than have been reported in most studies of actigraphy. Alternatively, the data might imply that individuals modify their self-reports about sleep via daily feedback to align their perceptions to the output of the wearable.