JMIR Publications

ABSTRACT

Background:

Poor sleep health is pervasive and contributes to long-lasting physical and psychological problems. As traditional sleep measurement can be burdensome, testing scalable and accessible sleep measurements is important.

Objective:

The aim of this study was to test whether sleep features obtained through a smartphone app are comparable to other modes of sleep measurement (i.e., daily diary, wearable actigraphy).

Methods:

College students (n=29) answered daily questions about their sleep and provided smartphone accelerometer data using the Effortless Assessment Research System (EARS) application for one week. Analyses compared bedtime, risetime, and time-in-bed across diary and EARS. Wrist wearable actigraphy in 13 participants was used in supplementary analyses.

Results:

On average, EARS showed a high true positive rate (86.6%) and low false positive rate (4.0%) in identifying bedtimes and risetimes. There was no significant difference among diary, actigraphy, and EARS bedtime, risetime, and time-in-bed (Ps≥.069). Day-to-day sleep features were significantly correlated between among diary, actigraphy, and EARS (rs≥0.29, Ps≤.001), except EARS and actigraphy risetimes (r=0.29, P=.067).

Conclusions:

Smartphone-based sleep sensors show acceptable alignment with more established methods and may provide a feasible alternative to measuring daily sleep patterns in a scalable way. Future studies will require larger, diverse samples to corroborate findings of concordance among EARS, diary, and actigraphy data in other populations.