Objectives: Smartphone applications are considered as the prime candidate for the purposes of large scale, low cost and long term sleep monitoring. How reliable is smartphone assessment of sleep remains a key issue and more validation studies with both healthy and patient populations are needed. In this study we compared the performance of four smartphone applications (Sleep Cycle-accelerometer; Sleep Cycle-microphone; Smart Alarm; Sense) with polysomnoghraphy (PSG). Our main objective was evaluating whether sleep reports provided by applications offer reliable assessment of standard sleep parameters and establishing which of the application features result more promising for personal home use. Methods: 20 healthy participants were recorded at home, for two consecutive nights. Four iPhone applications (two per each night) designed for sleep–wake detection were used simultaneously with PSG. Results: Pearson’s correlation coefficients between PSG parameters (Time in bed, TIB; Total Sleep Time, TST; Wake After Sleep Onset, WASO; Sleep Efficiency, SE; Sleep Latency, SL; NREM Sleep Stages 1-4, N1, N2, N3, N4; Slow Wave Sleep, SWS; Rapid Eye Movement Sleep, REMS) and app reports were calculated. Significant correlations are reported below. Sense: TIB (app) and TIB (PSG): r= .713, p=.003; TST (app) and TST (PSG): r= .777, p=.001; Sleep Score (app) and SE (PSG): r= .482, p=.069; Light Sleep (app) and N1+N2+ REM (PSG): r= .424, p=.062; Sleeping Soundly(app) and N3: r= .596, p=.019; Sleeping Soundly(app) and N4(PSG): r= .520, p=.047. Smart Alarm: TIB (app) and TIB (PSG): r= .944, p< .001; Time Awake (app) and WASO (PSG): r= .473, p=.035; Sleep Quality (app) and SE(PSG): r= .431, p=.057. Sleep Cycle-accelerometer: TIB(app) and TIB (PSG): r= .672, p=.002; Sleep quality (app) and SE (PSG): r= -.480, p=.038. Sleep Cycle-microphone: TIB (app) and TIB (PSG): r= .492, p=.045; Sleep quality (app) and TIB (PSG): r= -.522, p=.032. Conclusions: Two apps provided partially reliable estimates of SE and showed significant correlations with TST and WASO measured by PSG. Only one app showed significant correlations with SWS parameters. In general, the examined apps do not offer reliable sleep stage data, not discriminating light sleep from deep sleep and especially not providing any estimate of REM.

Can smartphone sleep applications reliably assess sleep-wake cycle? Preliminary findings from a PSG study.

Mazzetti, M;Fino, E;Pizza, F;Vandi, S;Plazzi, G.
2018

Abstract

Objectives: Smartphone applications are considered as the prime candidate for the purposes of large scale, low cost and long term sleep monitoring. How reliable is smartphone assessment of sleep remains a key issue and more validation studies with both healthy and patient populations are needed. In this study we compared the performance of four smartphone applications (Sleep Cycle-accelerometer; Sleep Cycle-microphone; Smart Alarm; Sense) with polysomnoghraphy (PSG). Our main objective was evaluating whether sleep reports provided by applications offer reliable assessment of standard sleep parameters and establishing which of the application features result more promising for personal home use. Methods: 20 healthy participants were recorded at home, for two consecutive nights. Four iPhone applications (two per each night) designed for sleep–wake detection were used simultaneously with PSG. Results: Pearson’s correlation coefficients between PSG parameters (Time in bed, TIB; Total Sleep Time, TST; Wake After Sleep Onset, WASO; Sleep Efficiency, SE; Sleep Latency, SL; NREM Sleep Stages 1-4, N1, N2, N3, N4; Slow Wave Sleep, SWS; Rapid Eye Movement Sleep, REMS) and app reports were calculated. Significant correlations are reported below. Sense: TIB (app) and TIB (PSG): r= .713, p=.003; TST (app) and TST (PSG): r= .777, p=.001; Sleep Score (app) and SE (PSG): r= .482, p=.069; Light Sleep (app) and N1+N2+ REM (PSG): r= .424, p=.062; Sleeping Soundly(app) and N3: r= .596, p=.019; Sleeping Soundly(app) and N4(PSG): r= .520, p=.047. Smart Alarm: TIB (app) and TIB (PSG): r= .944, p< .001; Time Awake (app) and WASO (PSG): r= .473, p=.035; Sleep Quality (app) and SE(PSG): r= .431, p=.057. Sleep Cycle-accelerometer: TIB(app) and TIB (PSG): r= .672, p=.002; Sleep quality (app) and SE (PSG): r= -.480, p=.038. Sleep Cycle-microphone: TIB (app) and TIB (PSG): r= .492, p=.045; Sleep quality (app) and TIB (PSG): r= -.522, p=.032. Conclusions: Two apps provided partially reliable estimates of SE and showed significant correlations with TST and WASO measured by PSG. Only one app showed significant correlations with SWS parameters. In general, the examined apps do not offer reliable sleep stage data, not discriminating light sleep from deep sleep and especially not providing any estimate of REM.
JOURNAL OF SLEEP RESEARCH
657
657
Mazzetti, M; Fino, E; Pizza, F ; Vandi, S; Plazzi, G.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/678070
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact