Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography

Abstract

Study Objectives: Several inexpensive, readily available smartphone apps that claim to monitor sleep are popular among patients. However, their accuracy is unknown, which limits their widespread clinical use. We therefore conducted this study to evaluate the validity of parameters reported by one such app, the Sleep Time app (Azumio, Inc., Palo Alto, CA, USA) for iPhones. Methods: Twenty volunteers with no previously diagnosed sleep disorders underwent in-laboratory polysomnography (PSG) while simultaneously using the app. Parameters reported by the app were then compared to those obtained by PSG. In addition, an epoch-by-epoch analysis was performed by dividing the PSG and app graph into 15-min epochs. Results: There was no correlation between PSG and app sleep effi ciency (r = -0.127, p = 0.592), light sleep percentage (r = 0 .024, p = 0 .921), d eep s leep p ercentage ( r = 0 .181, p = 0.444) or sleep latency (rs = 0.384, p = 0.094). The app slightly and nonsignifi cantly overestimated sleep effi ciency by 0.12% (95% confi dence interval [CI] -4.9 to 5.1%, p = 0.962), signifi cantly underestimated light sleep by 27.9% (95% CI 19.4-36.4%, p < 0.0001), signifi cantly overestimated deep sleep by 11.1% (CI 4.7-17.4%, p = 0.008) and signifi cantly overestimated sleep latency by 15.6 min (CI 9.7-21.6, p < 0.0001). Epochwise comparison showed low overall accuracy (45.9%) due to poor interstage discrimination, but high accuracy in sleep-wake detection (85.9%). The app had high sensitivity but poor specifi city in detecting sleep (89.9% and 50%, respectively). Conclusions: Our study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG. Further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility.