Literature DB >> 28656615

A pilot study of a novel smartphone application for the estimation of sleep onset.

Hannah Scott1, Leon Lack1,2, Nicole Lovato1,2.   

Abstract

The aim of the study was to investigate the accuracy of Sleep On Cue: a novel iPhone application that uses behavioural responses to auditory stimuli to estimate sleep onset. Twelve young adults underwent polysomnography recording while simultaneously using Sleep On Cue. Participants completed as many sleep-onset trials as possible within a 2-h period following their normal bedtime. On each trial, participants were awoken by the app following behavioural sleep onset. Then, after a short break of wakefulness, commenced the next trial. There was a high degree of correspondence between polysomnography-determined sleep onset and Sleep On Cue behavioural sleep onset, r = 0.79, P < 0.001. On average, Sleep On Cue overestimated sleep-onset latency by 3.17 min (SD = 3.04). When polysomnography sleep onset was defined as the beginning of N2 sleep, the discrepancy was reduced considerably (M = 0.81, SD = 1.96). The discrepancy between polysomnography and Sleep On Cue varied between individuals, which was potentially due to variations in auditory stimulus intensity. Further research is required to determine whether modifications to the stimulus intensity and behavioural response could improve the accuracy of the app. Nonetheless, Sleep On Cue is a viable option for estimating sleep onset and may be used to administer Intensive Sleep Retraining or facilitate power naps in the home environment.
© 2017 European Sleep Research Society.

Entities:  

Keywords:  behavioural sleep onset; consumer sleep apps; intensive sleep retraining; objective sleep measurement

Mesh:

Year:  2017        PMID: 28656615     DOI: 10.1111/jsr.12575

Source DB:  PubMed          Journal:  J Sleep Res        ISSN: 0962-1105            Impact factor:   3.981


  4 in total

Review 1.  Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence.

Authors:  Edita Fino; Michela Mazzetti
Journal:  Sleep Breath       Date:  2018-04-23       Impact factor: 2.816

2.  The accuracy of the THIM wearable device for estimating sleep onset latency.

Authors:  Hannah Scott; Ashwin Whitelaw; Alex Canty; Nicole Lovato; Leon Lack
Journal:  J Clin Sleep Med       Date:  2021-05-01       Impact factor: 4.062

Review 3.  Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review.

Authors:  Melissa Aji; Christopher Gordon; Elizabeth Stratton; Rafael A Calvo; Delwyn Bartlett; Ronald Grunstein; Nick Glozier
Journal:  J Med Internet Res       Date:  2021-02-17       Impact factor: 5.428

4.  Smartphone applications for sleep tracking: rating and perceptions about behavioral change among users.

Authors:  Reema A Karasneh; Sayer I Al-Azzam; Karem H Alzoubi; Sahar Hawamdeh; Anan S Jarab; Mohammad B Nusair
Journal:  Sleep Sci       Date:  2022 Jan-Mar
  4 in total

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