title: Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography.
abstract: OBJECTIVE
  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 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).
  
  
  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.
  
  
  CONCLUSIONS
  A commentary on this article appears in this issue on page 695.
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Sushanth
authors.initials: S
authors.lastname: Bhat
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Ambra
authors.initials: A
authors.lastname: Ferraris
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Divya
authors.initials: D
authors.lastname: Gupta
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Mona
authors.initials: M
authors.lastname: Mozafarian
authors.affiliation: Seton Hall University School of Health and Medical Sciences, South Orange, NJ.
authors.forename: Vincent A
authors.initials: VA
authors.lastname: DeBari
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Neola
authors.initials: N
authors.lastname: Gushway-Henry
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Satish P
authors.initials: SP
authors.lastname: Gowda
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Peter G
authors.initials: PG
authors.lastname: Polos
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Mitchell
authors.initials: M
authors.lastname: Rubinstein
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Huzaifa
authors.initials: H
authors.lastname: Seidu
authors.affiliation: JFK Neuroscience Institute/Seton Hall University, Edison NJ.
authors.forename: Sudhansu
authors.initials: S
authors.lastname: Chokroverty
mesh_terms: D000328:Adult; D015331:Cohort Studies; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D063731:Mobile Applications / Q000706:statistics & numerical data*; D015999:Multivariate Analysis; D017286:Polysomnography / Q000379:methods*; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*; D000068997:Smartphone / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: actigraphy; apps; iPhones and sleep; mobile phones and sleep; sleep apps; sleep cycle; smartphones and sleep