Comparison of Sleep Features Across Smartphone Sensors, Actigraphy, and Diaries Among Young Adults: Longitudinal Observational Study

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 (ie, daily diary and wearable actigraphy). Methods: Healthy college students (n=29, 18-24 years old) with no prior diagnosis of a sleep disorder consented to downloading a smartphone app, the Effortless Assessment Research System (EARS) (Center for Digital Mental Health, University of Oregon; Ksana Health). For 1 week, the EARS app collected data continuously using the phone's accelerometer, gyroscope, and exposure to light. Each morning, participants received a notification via EARS to complete a sleep diary, which asked participants what time they got into bed, fell asleep, woke up, and got out of bed. A random subset (n=13) of participants also consented to wear an ActiGraph wristwatch. All analyses examined bedtime (ie, time going to bed), risetime (ie, wake-up time), and time-in-bed (ie, duration between time-in-bed and out-of-bed) as 3 measures of interest. For all analyses, diary data were considered the reference measurement, and analyses were repeated with ActiGraph as the reference measurement. Results: On average, EARS showed a high mean true positive rate (86.6%) and low mean false positive rate (4%) based on diary-reported bedtime and risetime. Supplementary analyses comparing EARS to ActiGraph data showed a similar true positive rate (83.7%) and slightly higher false positive rate (8.5%). Although there were no significant differences in mean bedtime, risetime, and time-in-bed among sources (P≥.07), there was some misalignment. Compared to the diaries, EARS estimated bedtime to be later by an average of 20 minutes, risetime to be earlier by an average of 21.83 minutes, and time-in-bed to be shorter by an average of 41.82 minutes. Relative to ActiGraph estimates, EARS estimated bedtime to be earlier by an average of 21.87 minutes, but estimated risetime to be later by 2.5 minutes, and time-in-bed to be longer by 24.4 minutes. Day-to-day correlations showed that bedtimes, risetimes, and time-in-bed were positively correlated between diary and EARS (0.29≤r≤0.52, P≤.002). Similarly, day-to-day bedtimes and time-in-bed were positively correlated between ActiGraph and EARS (0.38≤r≤0.55, P≤.01), and while correlated in the expected direction, risetimes were not significantly associated between ActiGraph and EARS (r=0.29, P=.07). 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.

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