Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

• Published in: Journal of clinical sleep medicine Vol. 11; no. 7; pp. 709 - 715
• Main Authors: Bhat, Sushanth, Ferraris, Ambra, Gupta, Divya, Mozafarian, Mona, DeBari, Vincent A, Gushway-Henry, Neola, Gowda, Satish P, Polos, Peter G, Rubinstein, Mitchell, Seidu, Huzaifa, Chokroverty, Sudhansu
• Format: Journal Article
• Language: English
• Published: United States American Academy of Sleep Medicine 15.07.2015
• Subjects: Adult; Cohort Studies; Female; Healthy Volunteers; Humans; Linear Models; Male; Middle Aged; Mobile Applications - utilization; Multivariate Analysis; Polysomnography - methods; Scientific Investigations; Sensitivity and Specificity; Sleep Stages - physiology; Smartphone - utilization; Young Adult; mobile phones and sleep; iPhones and sleep; sleep cycle; sleep apps; smartphones and sleep; actigraphy; apps
• ISSN: 1550-9389; 1550-9397
• DOI: 10.5664/jcsm.4840

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. 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. There was no correlation between PSG and app sleep efficiency (r = -0.127, p = 0.592), light sleep percentage (r = 0.024, p = 0.921), deep sleep percentage (r = 0.181, p = 0.444) or sleep latency (rs = 0.384, p = 0.094). The app slightly and nonsignificantly overestimated sleep efficiency by 0.12% (95% confidence interval [CI] -4.9 to 5.1%, p = 0.962), significantly underestimated light sleep by 27.9% (95% CI 19.4-36.4%, p < 0.0001), significantly overestimated deep sleep by 11.1% (CI 4.7-17.4%, p = 0.008) and significantly 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 specificity in detecting sleep (89.9% and 50%, respectively). 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. A commentary on this article appears in this issue on page 695.